Algorithm Concept for Crash Detection in Passenger Cars 9783658293918, 9783658293925, 3658293918

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André Leschke

Algorithm Concept for Crash Detection in Passenger Cars

Algorithm Concept for Crash Detection in Passenger Cars

André Leschke

Algorithm Concept for Crash Detection in Passenger Cars

André Leschke Volkswagen Group (Germany) Wolfsburg, Germany Dissertation, University of Rome „Tor Vergata“, 2019 Disclaimer: The results, opinions and conclusions of this dissertation are not n­ ecessarily those of Volkswagen AG.

ISBN 978-3-658-29391-8 ISBN 978-3-658-29392-5  (eBook) https://doi.org/10.1007/978-3-658-29392-5 © Springer Fachmedien Wiesbaden GmbH, part of Springer Nature 2020 This work is subject to copyright. All rights are reserved by the Publisher, whether the whole or part of the material is concerned, specifically the rights of translation, reprinting, reuse of illustrations, recitation, broadcasting, reproduction on microfilms or in any other physical way, and transmission or information storage and retrieval, electronic adaptation, computer software, or by similar or dissimilar methodology now known or hereafter developed. The use of general descriptive names, registered names, trademarks, service marks, etc. in this publication does not imply, even in the absence of a specific statement, that such names are exempt from the relevant protective laws and regulations and therefore free for general use. The publisher, the authors and the editors are safe to assume that the advice and information in this book are believed to be true and accurate at the date of publication. Neither the publisher nor the authors or the editors give a warranty, expressed or implied, with respect to the material contained herein or for any errors or omissions that may have been made. The publisher remains neutral with regard to jurisdictional claims in published maps and institutional affiliations. This Springer Vieweg imprint is published by the registered company Springer Fachmedien Wiesbaden GmbH part of Springer Nature. The registered company address is: Abraham-Lincoln-Str. 46, 65189 Wiesbaden, Germany

Acknowledgement

This thesis was written in addition to my actual work as Head of Electronics and Testing for Vehicle Safety at Volkswagen AG. Naturally, the daily workload does not leave much room for dealing intensively with a scientific question during the working week. Therefore, my first thanks go to my family, my wife Simone and my children Janik and Niklas, who have endured without complaint that their husband and father has spent many family holidays, weekends and evenings of the last few years at the desk or laptop. Many thanks go to Prof. Dr. Stefan Kubica, with whom, many years ago, I had the idea of diving into the depths of research again after a long time. Thank you for your motivation and support over the last years and especially for your willingness to supervise the work from the German side within the scope of your work at the TH Wildau. Many thanks also to Prof. Vincenzo Bonaiuto, who supervised and supported me from the University of Rome "Tor Vergata". Many thanks to my colleague Florian Weinert, who has helped me over the past years as a discussion partner, supporter in testing and as a tireless LaTeX expert. Special thanks go to Prof. Dr. Ludwig Brabetz and Dr. Torsten Strutz, who have decisively shaped my professional life and have always been role models for an exemplary and human management culture. Finally, I would like to thank my parents, who have stood steadfastly by my side throughout my life in difficult and easy times and have always supported me unreservedly in all my projects. André Leschke

Contents

Acknowledgement

V

List of Figures

XIII

List of Tables

XVII

List of Diagrams Glossary and List of Abbreviations Symbols . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

XXI XXV XXV

Abbreviations . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . XXVIII

Abstract

XXXIII

1 Introduction

1

1.1 Motivation . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

1

1.2 Problems of the Work . . . . . . . . . . . . . . . . . . . . . . . . . . .

4

1.3 Objectives . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

6

1.4 Research Fields and Objects of Investigation . . . . . . . . . . . . .

7

1.5 Structure . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

9

I

State-of-the-Art

11

2 Basics of Traffic Safety

13

2.1 Traffic Safety in General . . . . . . . . . . . . . . . . . . . . . . . . .

13

2.2 Vehicle Safety . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

15

2.3 Development of Passive Vehicle Safety over Time . . . . . . . . . .

18

VIII

Contents

2.3.1

Vehicle Structure and Restraint Systems . . . . . . . . . . . .

18

2.3.2

Electronics and Sensors . . . . . . . . . . . . . . . . . . . . . . .

21

3 Vehicle Structure, Restraint and Electronic Systems

23

3.1 Vehicle Structure . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

23

3.2 Vehicle Interior . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

24

3.3 Restraint Systems . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

25

3.3.1

Seat Belts . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

25

3.3.2

Airbags . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

28

3.4 Electronic Systems for Crash Detection . . . . . . . . . . . . . . . .

29

3.4.1

Airbag Control Unit . . . . . . . . . . . . . . . . . . . . . . . . .

30

3.4.2

Crash Sensors . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

32

3.4.3

Signal Processing . . . . . . . . . . . . . . . . . . . . . . . . . .

37

3.5 Airbag Algorithms . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

39

3.5.1

Legal Requirements, Consumer Test, Field Situation . . . . .

39

3.5.2

Firing Requirements on Restraint Systems . . . . . . . . . . .

43

3.5.3

Crash Detection Basics . . . . . . . . . . . . . . . . . . . . . . .

46

3.5.4

Algorithm Based on Velocity Reduction . . . . . . . . . . . . .

52

II New Algorithm Concept and Simulation Model

61

4 New Algorithm Concept

63

4.1 Motivation for a New Algorithm Approach . . . . . . . . . . . . . .

63

4.2 Local Component-Specific Loads . . . . . . . . . . . . . . . . . . . .

66

4.3 Dynamic Behaviour of Cars in Various Accident Constellations .

68

4.3.1

Front End, Body Structure and Components . . . . . . . . . .

68

4.3.2

Requirements on an Algorithm . . . . . . . . . . . . . . . . . .

70

4.3.3

Measurands and Coordinate Systems . . . . . . . . . . . . . .

72

4.4 Comparison of Different Acceleration Signals . . . . . . . . . . . .

74

4.4.1

Influence of Collision Speed . . . . . . . . . . . . . . . . . . . .

76

4.4.2

Influence of the Collision Type . . . . . . . . . . . . . . . . . .

79

4.4.3

Influence of the Collision Direction . . . . . . . . . . . . . . .

82

Contents

IX

4.5 Duration of the Crash, Algorithm Runtime and Start Time . . . .

83

4.6 State-of-the-Art Algorithm Compared with the Requirements . .

85

5 Model for the Description of Threshold-Based Algorithms

87

5.1 Structure of Algorithms . . . . . . . . . . . . . . . . . . . . . . . . .

87

5.2 Classification Model of the Algorithm Block . . . . . . . . . . . . .

88

5.3 Evaluation of the State-of-the-Art in the Classification Model . .

91

6 Simulation Model for Component-Specific Local Load

93

6.1 Structure of the Simulation Model . . . . . . . . . . . . . . . . . . .

93

6.2 Signal Pre-Processing . . . . . . . . . . . . . . . . . . . . . . . . . . .

99

III Methods and Results

107

7 Algorithm for Local Component-Specific Load

109

7.1 Fundamentals of the Crash Intensity Algorithm . . . . . . . . . . .

109

7.2 Crash Intensity as a New Input Variable for the CI Algorithm . .

112

8 First Degree of Freedom: Holdmax Threshold 8.1 Methodology for Selecting the Holdmax Threshold . . . . . 8.1.1 Proximity Measures . . . . . . . . . . . . . . . . . . . . . . 8.1.2 Minkowski Metrics . . . . . . . . . . . . . . . . . . . . . . . 8.2 Determination of the Optimum Holdmax Threshold . . . . . 8.2.1 Criterion 1: Firing Times . . . . . . . . . . . . . . . . . . . 8.2.2 Criterion 2: Misuse Stability . . . . . . . . . . . . . . . . . 8.2.3 Criterion 3: Separability between Load Case Groups . . 8.2.4 Criterion 4: Separability within the Load Case Groups . 8.2.5 Summary . . . . . . . . . . . . . . . . . . . . . . . . . . . .

119 119 120 122 123 125 129 132 135 138

. . . . . . . . . . . . . . . . . . . . . . . . . . .

9 Data Duality of Crash Intensity Values

141

9.1 Model for Assessing the Selectivity . . . . . . . . . . . . . . . . . . .

141

9.2 Evaluation of Selectivity for the 6 m/s Holdmax Threshold . . . .

145

9.3 Methodology for Evaluating the Data Correlations . . . . . . . . .

148

X

Contents

9.4 Evaluation of CIT Values in Relation to Firing Time Requirements

151

9.5 Summary . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

155

10 Second Degree of Freedom: Selection of Sensors

159

10.1 Methodology for the Reduction of Strongly Correlating Sensors .

159

10.2 Reduction of the Sensor Pool by Strongly Correlating Sensors . .

161

10.3 Methodology for Optimising the Sensor Data Pool . . . . . . . . .

164

10.4 Application of the Methodology . . . . . . . . . . . . . . . . . . . .

169

10.5 Summary . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

172

11 Third Degree of Freedom: Application

177

11.1 Threshold Design Using a Double Regression Line as an Example

179

11.2 Comparison of Application Results with the State-of-the-Art . . .

188

11.3 Robustness of the Algorithm . . . . . . . . . . . . . . . . . . . . . . .

190

11.3.1 Variation of the Amplitude of the Measurement Signal . . .

190

11.3.2 Optimization of the Application Threshold . . . . . . . . . . .

195

11.3.3 Comparison of the Results with the State-of-the-Art . . . . .

197

11.3.4 Variation of the Load Case Set . . . . . . . . . . . . . . . . . . .

199

11.3.5 Firing Times of the New Load Cases . . . . . . . . . . . . . . .

200

11.4 Summary . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

201

12 Algorithm Concept for the Classification of Load Cases

205

12.1 Methodology for Determining the Sensors for Classification . . .

205

12.2 Classification of Wall 0° Load Cases . . . . . . . . . . . . . . . . . .

208

12.3 Classification of ODB Load Cases . . . . . . . . . . . . . . . . . . . .

216

12.4 Classification of Further Load Case Groups . . . . . . . . . . . . . .

221

12.5 Classification of the Hit Direction . . . . . . . . . . . . . . . . . . .

225

12.6 Summary . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

225

13 Two-Stage Algorithm to Minimize the Number of Sensors

229

13.1 Methodology . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

229

13.2 Maximum Reduction of Sensors . . . . . . . . . . . . . . . . . . . .

232

13.3 Classification of Pole and Truck Load Cases . . . . . . . . . . . . .

238

Contents

XI

13.4 Application of First Stage . . . . . . . . . . . . . . . . . . . . . . . .

243

13.5 Overall Result of a Two-Stage Algorithm . . . . . . . . . . . . . . .

243

14 Validation of the Algorithm in Real Crash Tests

247

14.1 Experimental Programme . . . . . . . . . . . . . . . . . . . . . . . .

247

14.2 Measurement Setup . . . . . . . . . . . . . . . . . . . . . . . . . . . .

247

14.3 Test Results . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

248

15 Summary and Qutlook

257

15.1 Answers to the Research Questions . . . . . . . . . . . . . . . . . .

257

15.2 Outlook . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

260

Bibliography

263

Accompanying Publications

271

List of Figures

1.1

Conventional airbag system of current vehicles . . . . . . . . . .

4

1.2

Destruction of vehicle front end during a frontal crash . . . . .

7

1.3

Research content of the work . . . . . . . . . . . . . . . . . . . . .

9

2.1

Three pillars of road safety . . . . . . . . . . . . . . . . . . . . . .

13

2.2

Overview over vehicle safety . . . . . . . . . . . . . . . . . . . . .

16

2.3

Structure of vehicle safety . . . . . . . . . . . . . . . . . . . . . . .

17

2.4

Patent "The principle of the rigid driver’s compartment" . . . . .

19

2.5

Development of seat belt technology . . . . . . . . . . . . . . . .

20

2.6

"Ball-in-Tube" sensor . . . . . . . . . . . . . . . . . . . . . . . . . .

22

3.1

Body structure . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

24

3.2

Restraint and electronic systems of current passenger cars . . .

26

3.3

Operating principle of a pyrotechnic seat belt pretensioner . . .

27

3.4

Operating principle of a seat belt force limiter . . . . . . . . . . .

27

3.5

Operating principle of airbag deployment . . . . . . . . . . . . .

28

3.6

Passenger car airbag system . . . . . . . . . . . . . . . . . . . . . .

29

3.7

Redundancy in the airbag control unit . . . . . . . . . . . . . . .

32

3.8

Airbag control unit . . . . . . . . . . . . . . . . . . . . . . . . . . .

33

3.9

Sensor concept of current passenger cars . . . . . . . . . . . . . .

34

3.10

Measuring principle of capacitive acceleration sensor . . . . . .

34

3.11

Integration of acceleration sensors on the circuit board . . . . .

36

3.12

External acceleration sensors . . . . . . . . . . . . . . . . . . . . .

36

3.13

External pressure sensors . . . . . . . . . . . . . . . . . . . . . . .

36

3.14

Principle image of capacitive pressure sensor . . . . . . . . . . .

37

XIV

List of Figures

3.15

Signal processing from sensor to algorithm . . . . . . . . . . . .

37

3.16

Different crash barriers . . . . . . . . . . . . . . . . . . . . . . . . .

43

3.17

Misuse tests . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

44

3.18

Method and control device . . . . . . . . . . . . . . . . . . . . . .

49

3.19

US Patent 5.040.118 R. Diller . . . . . . . . . . . . . . . . . . . . .

50

4.1

New requirements of test organisations and legislators . . . . .

64

4.2

Destruction behaviour at increasing speeds . . . . . . . . . . . .

65

4.3

Destruction behaviour in different load cases . . . . . . . . . . .

65

4.4

64 km/h ODB color-coded load wave . . . . . . . . . . . . . . . .

66

4.5

32 km/h Wall 0° color-coded load wave . . . . . . . . . . . . . . .

67

4.6

Body structure of medium-size vehicle . . . . . . . . . . . . . . .

69

4.7

Load paths of energy input into a vehicle . . . . . . . . . . . . . .

69

4.8

Structure of a front end of a medium-sized vehicle . . . . . . . .

70

4.9

Structure of the front end using of a small car vehicle . . . . . .

71

4.10

State-of-the-art algorithms compared to stated requirements .

85

5.1

Structure of threshold-based algorithms . . . . . . . . . . . . . .

88

5.2

Instance model . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

89

5.3

Graduated model . . . . . . . . . . . . . . . . . . . . . . . . . . . .

90

5.4

Time model . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

90

5.5

Evaluation of the concept in the algorithm model . . . . . . . .

91

6.1

Systematic distribution of measuring points . . . . . . . . . . . .

94

6.2

Plane 1 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

97

6.3

Plane 2 to Plane 11 . . . . . . . . . . . . . . . . . . . . . . . . . . .

98

6.4

True / False animation and heatmap . . . . . . . . . . . . . . . . .

106

7.1

Concept of the Crash Intensity algorithm . . . . . . . . . . . . . .

110

8.1

Proximity measures . . . . . . . . . . . . . . . . . . . . . . . . . . .

122

8.2

Number of sensors above the HMT at 2 m/s and 10 m/s . . . .

126

9.1

Overview of selected cluster methods . . . . . . . . . . . . . . . .

142

List of Figures

XV

9.2

Ideal correlation between Firing Time Requirement and CIT . .

150

9.3

Firtst acceptable correlation between FTR and CIT . . . . . . . .

151

9.4

Second acceptable correlation between FTR and CIT . . . . . .

152

10.1

Overview of the sensor pool after reduction to 92 sensors . . .

163

10.2

Overview of the sensor pool after reduction to 71 sensors . . .

173

11.1

Amplitude variation of the raw sensor signals by ±15% . . . . .

191

11.2

Four categories of signal variation influence on CIT values . . .

192

11.3

New load cases (Oblique and MPDB load case) . . . . . . . . . .

201

11.4

Model description of main algorithm for firing decision . . . . .

203

12.1

Wall 0° classification model . . . . . . . . . . . . . . . . . . . . . .

215

12.2

Time-dependent classification algorithms . . . . . . . . . . . . .

216

12.3

ODB classification model . . . . . . . . . . . . . . . . . . . . . . . .

221

12.4

Classification model of all algorithms for 71 sensors . . . . . . .

228

12.5

CI algorithm compared to stated requirements . . . . . . . . . .

228

13.1

Requirements on a two-stages firing time algorithm . . . . . . .

230

13.2

Two-stages firing algorithm . . . . . . . . . . . . . . . . . . . . . .

231

13.3

Reduced sensor pool for a two-stage algorithm . . . . . . . . . .

233

14.1

State-of-the-art load case set and selected test support points .

248

14.2

Sensors in the simulation and in the vehicle . . . . . . . . . . . .

249

14.3

Sensor measuring points in the vehicle before the test . . . . . .

249

14.4

32 km/h Wall 30° vehicle test . . . . . . . . . . . . . . . . . . . . .

250

List of Tables

3.1

Typical set of Misuse and NoFire load cases . . . . . . . . . . . .

40

3.2

Typical set of Fire load cases . . . . . . . . . . . . . . . . . . . . .

41

3.3

Classification of load cases . . . . . . . . . . . . . . . . . . . . . . .

42

3.4

Vehicle-specific triggering requirements . . . . . . . . . . . . . . .

54

3.5

Assignment of colours to the individual load case groups . . . .

55

6.1

Holdmax function time table . . . . . . . . . . . . . . . . . . . . .

105

6.2

Totals table . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

106

7.1

Holdmax and CI values for the 6 m/s and 10 m/s thresholds . .

115

7.2

CI, CIT and CITn values for four sensors . . . . . . . . . . . . . .

116

8.1

MCITn for the thresholds 2 m/s to 12 m/s . . . . . . . . . . . . .

124

8.2

Section of distance dimension matrix for the 6 m/s threshold .

125

8.3

Sensors of all planes . . . . . . . . . . . . . . . . . . . . . . . . . .

127

8.4

Sensors on the first plane . . . . . . . . . . . . . . . . . . . . . . .

128

8.5

Sensors on the second plane . . . . . . . . . . . . . . . . . . . . .

128

8.6

Section of MCITn values for Wall 30° and MN load cases . . . .

130

8.7

Section of proximity measures for Wall 30° and MN . . . . . . .

130

8.8

Evaluation criterion 2, scenario 1 . . . . . . . . . . . . . . . . . .

131

8.9

Evaluation of scenarios 2 to 5 . . . . . . . . . . . . . . . . . . . . .

133

8.10

Comparison of all results for criterion 2 . . . . . . . . . . . . . .

134

8.11

Evaluation criterion 3, scenario 1 . . . . . . . . . . . . . . . . . .

134

8.12

Comparison of all results for criterion 3 . . . . . . . . . . . . . .

135

8.13

Evaluation of criterion 4, partial scope of Wall 0°, scenario 1 . .

136

8.14

Comparison of all results for criterion 4 . . . . . . . . . . . . . .

137

XVIII

List of Tables

8.15

Overview of the overall result . . . . . . . . . . . . . . . . . . . . .

138

9.1

Evaluation according to Mojena for all load cases . . . . . . . .

146

9.2

Evaluation according to Mojena for Fire load cases . . . . . . . .

148

9.3

relationship between firing time and CIT . . . . . . . . . . . . . .

150

9.4

MCITn values at 10 ms for 219 sensors . . . . . . . . . . . . . . .

154

9.5

MCITn values at 32 ms . . . . . . . . . . . . . . . . . . . . . . . . .

156

9.6

MCITn values at 39 ms . . . . . . . . . . . . . . . . . . . . . . . . .

157

9.7

MCITn values for all load cases with 219 sensors . . . . . . . . .

158

10.1

Classification of the Pearson coefficient . . . . . . . . . . . . . . .

161

10.2

Section of the Pearson coefficient matrix at 300 ms . . . . . . .

162

10.3

Section of the Pearson coefficient matrix from 10 ms to 300 ms

162

10.4

MCITn values for all load cases with 92 sensors . . . . . . . . . .

164

10.5

MCITn values for all load cases with 71 sensors . . . . . . . . . .

171

10.6

Evaluation according to Mojena for all load cases . . . . . . . .

174

10.7

Evaluation according to Mojena for Fire load cases . . . . . . . .

175

11.1

Overall result for all application variants . . . . . . . . . . . . . .

187

11.2

Comparison of CIA to CVA . . . . . . . . . . . . . . . . . . . . . . .

189

11.3

Firing times with an amplitude variation of ±15% . . . . . . . .

193

11.4

Optimized firing times with an amplitude variation of ±15% .

198

11.5

Comparison CIA to CVA . . . . . . . . . . . . . . . . . . . . . . . .

199

11.6

Comparison of CIA to CVA . . . . . . . . . . . . . . . . . . . . . . .

203

12.1

Wilks’ Lambda values . . . . . . . . . . . . . . . . . . . . . . . . . .

207

12.2

Delta of CIT values . . . . . . . . . . . . . . . . . . . . . . . . . . .

208

12.3

Division into load case groups for Wall 0° classification . . . . .

209

12.4

Wilks’ Lambda values for Wall 0° . . . . . . . . . . . . . . . . . . .

211

12.5

Analysis of the Delta CI values for the Wall 0° classification . .

212

12.6

Classification times of the Wall 0° load cases . . . . . . . . . . . .

212

12.7

Analysis of the Delta CI values of the Wall 0° load cases . . . . .

214

12.8

Wilks’ Lambda for discrimination of ODB load cases . . . . . . .

217

List of Tables

12.9

XIX

Delta CI values for the ODB classification at 30 ms . . . . . . . .

218

12.10 Delta CI values for the ODB classification at 40 ms . . . . . . . .

219

12.11 Classification times of the ODB load cases . . . . . . . . . . . . .

220

12.12 Classification times of Wall 0° load cases . . . . . . . . . . . . . .

221

12.13 Sensor selection for Pole, Truck and Wall 30° classification . . .

222

12.14 Classification times of Pole, Wall 30° and Truck load cases . . .

222

12.15 Threshold exceedance times for Wall 0° load cases . . . . . . . .

224

12.16 Sensor selection for classification of left and right hit position .

225

12.17 Classification times for left and right hit position . . . . . . . . .

227

13.1

Evaluation of load case separability according to Mojena . . . .

233

13.2

MCITn values for 17 sensors . . . . . . . . . . . . . . . . . . . . .

234

13.3

Firing times with nominal signal and amplitude variation . . .

237

13.4

Wilks’ Lambda values of the Pole load case . . . . . . . . . . . . .

238

13.5

Analysis of the Delta CI values for the Pole load case . . . . . .

239

13.6

Classification times of the Pole and the Wall 0° load cases . . .

240

13.7

Wilks’ Lambda values of the Pole load case . . . . . . . . . . . . .

241

13.8

Analysis of the Delta CI values for the Truck load case . . . . . .

242

13.9

Classification times of the Wall 0° and Truck load cases . . . . .

242

13.10 Firing times of Pole and Truck load cases . . . . . . . . . . . . . .

244

13.11 Firing times of all load cases in a two-stage algorithm . . . . . .

245

14.1

Firing time results of crash tests . . . . . . . . . . . . . . . . . . .

253

14.2

Firing time result of Pole load case with shifted threshold . . .

255

List of Diagrams

1.1

Number of road traffic deaths and mileage . . . . . . . . . . . . .

2

1.2

Development over time of road traffic death worldwide . . . . .

2

1.3

Road traffic death per region in 2015 . . . . . . . . . . . . . . . .

3

1.4

Signal profile of RCAR 16 km/h versus ODB 64 km/h . . . . . .

5

3.1

Determining the FT of a LC from the forward displacement . .

54

3.2

Velocity in various selected crash scenarios . . . . . . . . . . . .

56

3.3

Crash application acc. to Olsson . . . . . . . . . . . . . . . . . . .

57

3.4

Velocity reduction on the centre tunnel . . . . . . . . . . . . . . .

57

3.5

Velocity reduction at an external sensor . . . . . . . . . . . . . .

59

3.6

Influence of information from other sensors on thresholds . . .

59

4.1

Overall vehicle deceleration and resulting velocity . . . . . . . .

73

4.2

Velocity of the occupant and resulting forward displacement . .

74

4.3

AC signals from centre tunnel sensor and front end sensor . . .

75

4.4

DC signals of three sensors in the front end . . . . . . . . . . . .

76

4.5

DC and velocity signals of the overall vehicle . . . . . . . . . . .

77

4.6

DC and velocity signals at the outer skin of the bumper . . . . .

78

4.7

DC and velocity signals at the side member . . . . . . . . . . . .

78

4.8

DC and velocity signals of the overall vehicle . . . . . . . . . . .

80

4.9

Velocity signals of the overall vehicle . . . . . . . . . . . . . . . .

80

4.10

DC and velocity signals at the outer skin of the bumper . . . . .

81

4.11

DC and velocity signals at the side member . . . . . . . . . . . .

81

4.12

DC and velocity signals of the overall vehicle . . . . . . . . . . .

82

4.13

DC and velocity signals at the side beam . . . . . . . . . . . . . .

83

XXII

List of Diagrams

4.14

Velocity signals for different load case types . . . . . . . . . . . .

84

6.1

Three load cases at four selected sensors . . . . . . . . . . . . . .

99

6.2

Example of an unfiltered local deceleration signal . . . . . . . .

100

6.3

Example of a filtered local deceleration signal . . . . . . . . . . .

100

6.4

Example of a local velocity reduction signal . . . . . . . . . . . .

101

6.5

Local velocity reduction curves for a 56 km/h Wall 0° LC . . . .

102

6.6

Holdmax post-processing step . . . . . . . . . . . . . . . . . . . .

103

6.7

Matlab tool chain . . . . . . . . . . . . . . . . . . . . . . . . . . . .

104

7.1

Holdmax conversion . . . . . . . . . . . . . . . . . . . . . . . . . .

114

7.2

CI determination of a sensor . . . . . . . . . . . . . . . . . . . . .

115

9.1

Example of a dendrogram . . . . . . . . . . . . . . . . . . . . . . .

144

9.2

Selection of the best clustering . . . . . . . . . . . . . . . . . . . .

145

9.3

Scree plot 300 ms . . . . . . . . . . . . . . . . . . . . . . . . . . . .

146

9.4

Two cluster solution for 300 ms . . . . . . . . . . . . . . . . . . . .

147

9.5

Two cluster solution 300 ms Fire load cases . . . . . . . . . . . .

149

9.6

CITn values for 33 load cases and 219 sensors . . . . . . . . . .

153

9.7

CITn values and optimization potential . . . . . . . . . . . . . . .

153

10.1

Nearly ideal regression line . . . . . . . . . . . . . . . . . . . . . .

166

10.2

Formation of the residual . . . . . . . . . . . . . . . . . . . . . . .

167

10.3

Regression analysis for the selection of sensors . . . . . . . . . .

168

10.4

Regression analysis of 92 sensors at 39 ms . . . . . . . . . . . . .

170

10.5

Sensor with the highest coefficient of determination at 39 ms .

170

10.6

Sensor with high potential for the 35 km/h Pole case . . . . . .

172

10.7

Two clusters 300 ms Fire and NM load cases with 71 sensors .

174

10.8

Two clusters 300 ms Fire load cases with 71 sensors . . . . . . .

175

11.1

CITn values for all load cases with 71 sensors . . . . . . . . . . .

178

11.2

Regression of the Wall 0° load cases . . . . . . . . . . . . . . . . .

180

11.3

Application threshold of the Wall 0° load cases . . . . . . . . . .

180

List of Diagrams

XXIII

11.4

Regression of LCs with slow crash severity progress . . . . . . .

181

11.5

Application threshold of LCs with slow crash severity progress

181

11.6

Critical intersections of the application lines . . . . . . . . . . . .

182

11.7

Representation of all three application thresholds for all LCs . .

183

11.8

Representation of the total application threshold for all LCs . .

183

11.9

Delimitation of the application threshold against NoFire LCs . .

184

11.10 Optimisation potential of robustness . . . . . . . . . . . . . . . .

185

11.11 4th threshold for optimization of robustness . . . . . . . . . . . .

186

11.12 Final application threshold . . . . . . . . . . . . . . . . . . . . . .

186

11.13 Amplitude variation by +15% . . . . . . . . . . . . . . . . . . . . .

194

11.14 Amplitude variation by -15% . . . . . . . . . . . . . . . . . . . . .

195

11.15 Threshold optimization . . . . . . . . . . . . . . . . . . . . . . . . .

196

11.16 Total application with nominal signals and ±15% . . . . . . . .

197

11.17 Application of the three new LC groups . . . . . . . . . . . . . . .

202

12.1

Wall 0° classification for nominal signals . . . . . . . . . . . . . .

210

12.2

Wall 0° classification for nominal and ±15% signals . . . . . . .

213

12.3

Classification of ODB LCs at an amplitude variation . . . . . . .

220

12.4

Classification of the Pole load case for nominal signals . . . . .

223

12.5

Classification of the Wall 30° load cases . . . . . . . . . . . . . . .

223

12.6

Classification of the Truck load case +15% amplitude variation

224

12.7

Classification of load cases with left hit position . . . . . . . . .

226

12.8

Classification of load cases with right hit position . . . . . . . .

226

13.1

Dendrograms with reduced sensor set . . . . . . . . . . . . . . . .

235

13.2

Regression line, load cases with fast crash severity progress . .

236

13.3

Regression line, load cases with slow crash severity progress . .

236

13.4

Application for 17 sensors for nominal signals . . . . . . . . . . .

237

13.5

Classification of the Pole load case with nominal signal values .

240

13.6

Classification of Truck load case . . . . . . . . . . . . . . . . . . .

243

13.7

Threshold adaptation for triggering the Pole and Truck LC . . .

244

14.1

32 km/h Wall 30° vehicle test . . . . . . . . . . . . . . . . . . . . .

251

XXIV

List of Diagrams

14.2

64 km/h ODB vehicle test . . . . . . . . . . . . . . . . . . . . . . .

252

14.3

Application for three vehicle tests . . . . . . . . . . . . . . . . . .

252

14.4

Classification of the Pole crash test . . . . . . . . . . . . . . . . . .

254

14.5

Application for Pole load case with threshold adjustment . . . .

254

Glossary and List of Abbreviations

Symbols C

capacity

Dk,l

distance of object k and l

E

energy

LF

considered load case

P

power

R2

coefficient of determination

T

sampling step

T1

number of sampling steps in period t 1

λ

Wilk coefficient

τ

time constant

a˜

Mojena Coeffizient

a˜i

standadized coefficient of fusion

a

acceleration

ai

Distance (error sum of squares)

XXVI

Glossary and List of Abbreviations

b0

intersection point with y-axis

b1

slope of lines

cov

covariance

f max

maximum frequency

fsamp

sampling rate

h

level of the Holdmax threshold

j

jerk

m

mass

n

number of load cases

r

Pearson coefficient

s

considered sensor

sx

standard deviation of variable x

s2x

variance of variable x

sy

standard deviation of variable y

s2y

variance of variable y

t

sampling time

t1

considered sampling time

t f ire

firing time

v

velocity

Symbols

XXVII

x

average sensor x

xi

value of load case on sensor x

x k, j

value of variable j on object k

x l, j

value of variable j on object l

y

estimation of CIn value

yi

value of load case on sensor y

27W

27 km/h Wall 0°

30T

30 km/h Truck

32W

32 km/h Wall 0°

32Wl

32 km/h Wall 30° left

32Wr

32 km/h Wall 30° right

35P

35 km/h Pole

40Ol

40 km/h ODB left

40Or

40 km/h ODB right

40W

40 km/h Wall 0°

40Wl

40 km/h Wall 30° left

49Wr

40 km/h Wall 30° right

50W

50 km/h Wall 0°

56Ol

56 km/h ODB left

XXVIII

Glossary and List of Abbreviations

56Or

56 km/h ODB right

56W

56 km/h Wall 0°

64Ol

64 km/h ODB left

64Or

64 km/h ODB right

Abbreviations A A-D

Analog-Digital

AC

acceleration

AEB

Autonomous Emergency Braking

B B

bottom

BT

Battery

C CFT

Crash Firing Time

CI

Crash Intensity

CIA

Crash Intensity Algorithm

CIT

Crash Intensity Total

CITn

Crash Intensity Total normalized

CO

Component

Abbreviations

XXIX

CT

Classification Time

CT

classification time

CV

Complete Vehicle

CVA

Complete Vehicle Algorithm

D DC

deceleration

DFE

deformation element

E E

Plane

ECU

Electronic Control Unit

ESC

Electronic Stability Control

Euro NCAP

European New Car Assessment Programme

F F

front area / bumper

FBD

strut dome

FP

Fast crash severity Progress

FT

Firing Time

FTR

firing time requirement

H

XXX

Glossary and List of Abbreviations

H

Height

HMT

Holdmax threshold

K K

Coordinate

KFA

fender suspension

KPV/H

radiator package front / rear

L LC

Load case

LDW

Lane Departure Warning

LT

side member

M M

mid

m

minus (vehicle coordinate system)

MB/V/H

engine block, side / front / rear

MCITn

Mojena Crash Intensity Total normalized

MN

Misuese / NoFire

MPDB

Moving Progressive Deformable Barrier

N NCAP

New Car Assessment Programme

Abbreviations

XXXI

O ODB

Offset Deformable Barrier

P p

plus (vehicle coordinate system)

Q QT

cross member

R RCAR

Research Councils for Automobile Repairs

RI

wheel arch inside

S S

Sensor name

SP

Slow crash severity Progress

SW

bulkhead

SWF

headlights

T T

top

U UN

United Nation

XXXII

US NCAP

Glossary and List of Abbreviations

American New Car Assessment Programme

W WHO

World Health Organization

Abstract

Airbag algorithms based on the current state-of-the-art make a decision to fire restraint systems in a crash on the basis of an evaluation of the deceleration of the entire vehicle in the progress of the accident. In order to meet the everincreasing requirements of consumer test organizations and global legislators, detailed knowledge of the nature and direction of the crash would be of great benefit. The algorithms used in current vehicles are only able to do this to a limited extent. In the context of this work, a completely different measurement method is presented to solve these problems. On the basis of this measurement method a new algorithm approach becomes possible. In addition to vehicle deceleration, the chronological sequence of an accident and the associated local and temporal destruction of the vehicle are possible indicators of the severity of an accident. In order to convert this behaviour into a new algorithm concept, the typical structure of a front end is investigated, and a model for the evaluation of local component-related loads, as an equivalent measurand for the destruction behaviour in the course of a crash, is created. As a result of the investigations, crash intensity is defined as a new evaluation variable for a crash algorithm. On the basis of this new evaluation variable, methods are proposed with the help of which an algorithm can be developed that enables the timely firing of restraint systems for each of the crash load cases under consideration. In addition, algorithms are designed that are able to classify crash load cases and provide information about the direction of the accident. The new algorithms are evaluated on the basis of simulation results, compared with the state-of-the-art, and the effectiveness of the overall algorithm concept is proven in real vehicle tests.

XXXIV

Abstract

In addition to the objects of investigation, the state-of-the-art in the field of vehicle safety, restraint systems and, in particular, the current algorithms in use are presented at the beginning of the work. The current algorithm concepts are classified according to a definition created within the scope of this work. Their advantages and disadvantages are evaluated and compared with the newly developed approach. Finally, an outlook is given on the fields of application of the methodology and on its possible further development. The overall result of the work is the provision of a validated algorithm concept for the timely firing of restraint systems in a crash, with simultaneous classification according to accident type and accident direction.

1 Introduction In this chapter, the basic motivation to deal with a topic from the field of classic passive vehicle safety is presented. The objectives and research questions are explained and the structure of the work is described.

1.1 Motivation Great progress has been made in recent years in the field of vehicle safety. A large number of measures, such as optimizing the body structure and restraint systems or equipping vehicles with chassis control systems, have contributed to this. [Seiffert, 2004] Since the desire for individual mobility is causing the global automobile population to grow steadily, the current passenger car population will double again by 2030. By 2030, two billion vehicles are expected to have been registered. [Statistisches Bundesamt, 2016] If highly industrialised countries such as Germany are considered, the improvement in road safety is reflected in the statistical accident data. Despite a significant increase in vehicle mileage, the number of road fatalities is falling continuously. But even in Germany, there are still great efforts to further reduce the number of road fatalities, especially since after long years of declining numbers no further progress has been seen for some years (Diagram 1.1). The global picture is different, especially outside the highly industrialised countries. There, the number of road traffic deaths has been stagnating for years at a very high level. [Peden, 2004] According to the latest World Health Organization (WHO) figures, there were 1.25 million road deaths worldwide in 2013 (Diagram 1.2). Diagram 1.3 clearly shows that regions with rather low incomes are overrepresented here. [World Health Organization, 2015] In the medium and long term, the increase in private transport, especially in developing countries, will lead to a further increase in the number of deaths on the roads worldwide. The decisive factors here are above all the inadequate © Springer Fachmedien Wiesbaden GmbH, part of Springer Nature 2020 A. Leschke, Algorithm Concept for Crash Detection in Passenger Cars, https://doi.org/10.1007/978-3-658-29392-5_1

2

Chapter 1 Introduction

Diagram 1.1: Number of road traffic deaths and mileage in the Federal Republic of Germany based on [Statistisches Bundesamt, 2016]

Diagram 1.2: Development over time of road traffic death worldwide based on [World Health Organization, 2015; International Traffic Safety Data and Analysis Group (IRTAD), 2006]

1.1 Motivation

3

Diagram 1.3: Road traffic death per region in 2015 based on [World Health Organization, 2015; Murray, 1996]

transport infrastructure and the use of older vehicles without state-of-the-art safety technology. Both the European Union and the Decade of Action proclaimed by the United Nations (UN) have set the goal of significantly reducing the number of accident fatalities. In concrete terms, the White Paper of the European Union speaks of halving the number of people killed in traffic within a period of 10 years. [World Health Organization, 2015; Europäische Union, 2011] In order to meet these requirements and implement the vision of virtually accidentfree road traffic, the consistent further development of safety systems for the protection of all persons involved in an accident is at the forefront of vehicle safety. On the other hand, especially in developing countries, the improvement of transport infrastructure and adequate training of road users is an important factor in improving road safety. Although the use of active safety systems such as Electronic Stability Control (ESC), Autonomous Emergency Braking (AEB) systems or lane departure warning systems means that active safety is playing an ever-increasing role in improving road safety, classic passive safety with a stable vehicle structure and restraint systems such as airbags and seat belts continues to form the indispensable basis of a safe vehicle.

4

Chapter 1 Introduction

Figure 1.1: Conventional airbag system of current vehicles

1.2 Problems of the Work The central component of today’s airbag systems is the airbag control unit. During an accident, the highly complex algorithms of this control unit evaluate the severity of the crash and differentiate between frontal, side and rear collisions. On the basis of this evaluation, the appropriate triggering times for the restraint systems installed in the vehicle, such as airbags and belt pretensioners, can then be calculated. Depending on the type of accident, triggering decisions must be made within a time interval of approx. 10 ms - 50 ms after initial contact with the oponent of the accident. One of the most important decision factors of current algorithm concepts is the measurement of the overall vehicle deceleration, which is determined using acceleration sensors (Figure 1.1). A fundamental challenge of this measuring principle is that the deceleration at overall vehicle level does not represent a direct measurand for a crash. For example, decelerations caused by Misuse events such as accidents involving wildlife, driving through large potholes or slipping onto a curb can also lead to sudden vehicle deceleration. Here, it is important to make a correct decision as far as possible so as not to trigger the vehicle’s restraint systems without

1.2 Problems of the Work

5

Diagram 1.4: Signal profile of RCAR 16 km/h versus ODB 64 km/h

justification and benefit. Especially in the extremely short time intervals after the start of an accident, the overall vehicle decelerations due to a Misuse event or a low-speed accident involving a hard object, such as a parking bollard, and the decelerations due to a higher-speed crash with another vehicle only differ marginally. The velocity reduction over time for two of these different types of accidents is illustrated in Diagram 1.4 the 64 km/h ODB load case (Offset Deformable Barrier), with its necessity to obtain a decision on the ignition of the restraint systems in the range between 25 ms and 32 ms, results in a lesser velocity reduction in the relevant time window than the 16 km/h RCAR test (Research Councils for Automobile Repairs), in which the restraint systems must not be triggered under any circumstances. The reason for this behaviour lies in the physical structure of the "accident opponents". A massive, hard object, such as a parking bollard or a concrete partition, leads directly to the deceleration of the impacting vehicle. A collision with another passenger car only leads to minor deceleration at the beginning of the accident due to the soft, energy-absorbing "crumple zone" of the opponent, until finally, in this example, the hard vehicle structure of the opponent becomes effective from approx. 62 ms onwards. A correct triggering decision cannot be made on the basis of this input variable alone.

6

Chapter 1 Introduction

In addition, the capability of acceleration sensors to provide information on the exact direction of the opponent to the accident is very limited. Another disadvantage of current algorithm concepts is that accidents cannot be grouped by opponents (described in more detail later in this paper). For example, it may be advantageous for the release of restraint systems to distinguish an accident involving another vehicle from an accident involving a rigid obstacle. On the basis of this information, restraint systems could be used in an even more targeted manner in the event of future, more stringent requirements.

1.3 Objectives The objective of this work is to develop a fundamentally new approach to frontal crash detection. In principle, the procedure can also be adapted for side and rear accidents. In addition to the deceleration of the overall vehicle, the chronological sequence of an accident and the associated local and temporal destruction of the vehicle is also an indicator of the severity and type of an accident [Kramer, 2009]. Figure 1.2 shows the destruction of the front end for a typical accident type as the crash speed increases. In both cases, the progress of the crash between 0 ms and 90 ms is shown. It is evident, that the destruction of the front end increases as the crash speed increases. The place of destruction on the other hand remains essentially constant. As this destruction behaviour is seen on a large number of crash tests of the same vehicle type, it is evident that the progress over time is always the same within the spread that is to be expected. This behaviour shall be used for a new algorithm approach for crash detection. The objective is to directly resolve the loads in the front end that are caused by the intrusions in the vehicle structure temporally and locally - and thus to generate a measurand that is proportional to the progress of the accident. On the basis of this measurand, an algorithm concept is developed which, in addition to the timely triggering of restraint systems in the event of an accident, also offers the possibility of classifying accidents according to their type and direction.

1.4 Research Fields and Objects of Investigation

7

Figure 1.2: Destruction of vehicle front end during a frontal crash at 40 km/h and 64 km/h

1.4 Research Fields and Objects of Investigation The objectives shown in Section 1.3 are worked on within the scope this PhD thesis. The systematic analysis of the behaviour of the front end in vehicle accidents and the development of an algorithm concept derived from this are to answer the following research questions: 1. Does the destruction and the resulting load on components in the front end correlate with the type of crash and the speed of the opponent? 2. Can the load on the local components in the front end be measured in such a way that a new evaluation variable can be developed from the measurement signals, with the aid of which a timely triggering of restraint systems for different accident types and speeds is possible using an algorithm? 3. Is the evaluation parameter developed in this thesis sufficient to develop advanced algorithms that classify the considered load cases according to the type of load case and its direction?

8

Chapter 1 Introduction

4. Can the described algorithms be built up in a single step on the basis of a single measurand? To answer the research questions, three major research topics will be dealt with within the framework of the work: • Simulation and measurand The structure of the front end is investigated and a simulation model for the evaluation of local component-specific loads is created. The simulation setup is applied to a specific vehicle concept and the corresponding simulation data is determined for each of the crash load cases considered. On the basis of the data, a new evaluation variable is proposed, based on which the new algorithms to be developed are built. • Algorithms On the basis of the new evaluation variable, methods are proposed with the help of which an algorithm can be developed which enables timely triggering of restraint systems for each of the crash load cases under consideration. In addition, algorithms are developed that are able to classify the load cases and provide information on the direction of the accident. Furthermore, methods are proposed to minimize the number of required measurement sensors. Finally, a methodology is proposed by which the number of required sensors can be significantly reduced yet further on the basis of a multi-stage algorithm. The results of the algorithms are evaluated on the basis of the simulation results and compared with the state of the art. • Validation The effectiveness of the algorithms developed within the scope of the work is proven in the real vehicle test. On the basis of a test matrix which represents all design load cases, the result quality and applicability of the algorithm are validated in real tests. The measurement data obtained in the test are pre-processed according to the algorithm concept and processed in the various new algorithms. The results are compared with the research questions and the effectiveness of the new overall procedure is demonstrated. In addition to the objects of investigation, the state of the art in the field of vehicle safety, restraint systems and, in particular, the current algorithms in use will be presented at the beginning of the work. The current algorithm concepts are classified according to a definition developed within the scope of this work,

1.5 Structure

9

Figure 1.3: Research content of the work

their advantages and disadvantages are presented and compared with the new approach to be developed. Finally, an outlook is given on the fields of application of the methodology and its possible further development. Figure 1.3 shows the research content of the work as a whole. The overall result of the work is the provision of a validated algorithm concept for the timely triggering of restraint systems in a crash, with simultaneous classification according to accident type and accident direction.

1.5 Structure After the introduction, in which the objectives of the work, the research questions and the structure of the work are described, the paper is divided into three parts. The first part "State-of-the-art" details the state of the art in the field of research. Starting with general traffic safety, the special vehicle safety, the vehicle structure,

10

Chapter 1 Introduction

the restraint systems and the sensors and electronic components used in current vehicles are all taken into account. Furthermore, accident algorithms are part of this chapters. In the second part "New algorithm concept and simulation model", the basic approach of the new algorithm concept that is based on local component-specific loads is explained. The basic structure of a front end is presented. Based on different measuring points in the vehicle, the behaviour of the vehicle in relation to different crash characteristics, such as crash type, crash speed and crash direction, is investigated and the influence of these variables on the algorithms is presented. In the following, a simulation model based on the geometric structure of the front end is created, a tool chain for further processing of the simulation data is presented and the simulation results for the load cases considered are generated. On the basis of the simulation results, a new evaluation variable "Crash Intensity" (CI) is proposed as the basis for the new algorithm concept. In the third part "Methods and results", a methodology is proposed at the beginning in order to determine the ideal crash intensity threshold for the considered vehicle model from the simulation data. In the following it is shown that the crash intensity as an input variable has a sufficient information quality to fulfil the requirements of an algorithm. For this purpose, a methodology for evaluation is proposed and applied. In further chapters, an overall algorithm concept to determine the firing times of the restraint systems in the crash, the classification by type of accident and by direction of the accident is presented and applied. It is shown how an application is created in the new algorithm concept and the results are compared with the state of the art. To evaluate the robustness of the new algorithms, the input data for the simulation are varied by ±15% and the algorithm is tested with new load cases that are not included in the application. Furthermore, a methodology is proposed how the number of required sensors can be significantly reduced by using a multi-stage algorithm concept. The results are evaluated and compared with the state of the art. To validate the algorithms, various tests are carried out at overall vehicle level and the sensor signals determined during these tests are evaluated with the newly created algorithms. The results are compared with the requirements and the suitability of the overall algorithm concept for the real application is presented. Finally, the answers to the research questions are explained and an outlook is given.

Part I

State-of-the-Art

2 Basics of Traffic Safety This chapter deals with the various aspects of traffic safety. Different approaches will be shown how the field of traffic safety can be classified. In addition, the historical development of passive vehicle safety in the areas of restraint systems, vehicle structure and electronics will be presented.

2.1 Traffic Safety in General "Traffic is the movement of persons, goods or messages in a defined system" [Ammoser and Hoppe, 2006]. Traffic thus includes all persons involved in traffic in their various roles as drivers, passengers, pedestrians or cyclists.

Figure 2.1: Three pillars of road safety

In addition, the different means of transport such as car, train, plane or ship must be considered here, as well as the associated traffic space, such as the road, © Springer Fachmedien Wiesbaden GmbH, part of Springer Nature 2020 A. Leschke, Algorithm Concept for Crash Detection in Passenger Cars, https://doi.org/10.1007/978-3-658-29392-5_2

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water or airways. If these considerations are restricted to the road traffic and the associated road safety which is the focus of further attention, it is necessary to consider the three main aspects of vehicle, driver and infrastructure (Figure 2.1). [Kramer, 2013] Only through the consistent further development of all three pillars of road safety can the number of people killed and injured in road traffic be improved holistically [Stieniczka, 2006]. Below are some examples of the potential for improvement for each of these three pillars: Driver • Training for driving a passenger car • Traffic education at schools and kindergartens • Verification of driving ability (for example drug / alcohol tests) Vehicle • Introduction of accident prevention systems such as emergency brake or electronic stability control • Improvement of vehicle structure • Improvement of restraint systems incl. electronics and sensor technology required for triggering • Improvement of outdoor lighting technology Infrastructure • Improvement of road infrastructure • Introduction of traffic flow and speed adaptation systems • Structural separation of traffic routes for pedestrians / cyclists and motorised road users • Improvement of rescue chain

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The following example clearly shows that all three sub-areas influence or complement each other and therefore cannot be considered independently of each other. Modern automatic lane-keeping systems, which are essentially designed to prevent accidents with unintentional off-road departures, use imaging sensors such as cameras to detect and predict the lane. An essential basis for this is to separate the drivable road course from the roadside in the camera images. These systems function most stably when the roadside is clearly demarcated by road lines. Most road accidents occur on country roads where road markings are much less common than on motorways. The safest way to achieve optimum road safety here would be to improve the interaction between vehicle and infrastructure. [Gonter and Leschke, 2016]

2.2 Vehicle Safety Vehicle safety can be divided into the areas shown in Figure 2.2. [Gonter and Leschke, 2016]. First basic definitions were developed by Wilfert and Seiffert [Seiffert, 1992, 1998]. The classification shown in Figure 2.2 distinguishes between two basic areas. Systems and components to prevent accidents (accident prevention) and systems and components to reduce the consequences of accidents (accident mitigation). In the area of accident prevention, the triad of people, vehicles and environment already described in the previous chapter can be found. The area of safety that is related to reducing the consequences of accidents can be divided into three sub-areas according to [Seiffert, 1998]. Before the accident (PreCrash) This sub-area contains measures that condition the vehicle and driver for a possible accident even before the actual collision. The typical approach here is to prepare components that are needed in the event of a collision and that have to take effect within a few milliseconds. If the collision can be avoided, these reversible systems can be returned to their normal driving condition. Examples include seat belts operated by an electric motor, seat settings or closing the windows before an accident.

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Figure 2.2: Overview over vehicle safety [Gonter and Leschke, 2016]

During the accident This sub-area contains measures which, during an accident, serve to minimise the effects on vehicle occupants, but also on opponents in passenger car crashes and on other accident participants (such as pedestrians and cyclists). In the area of self-protection, these include above all, all types of restraint systems such as airbags and seat belts, but also a vehicle structure that is designed to be optimal for accidents. When protecting the opponent in a passenger car, vehicle structures can be designed in such a way that the opponent’s vehicle is loaded as little as possible in the event of a collision; this is called structural compatibility. There are also measures to reduce the severity of injuries in accidents involving pedestrians or cyclists. In addition to geometric design details, such as the avoidance of sharp-edged contours or the creation of deformation elements like soft hoods, systems such as windscreen airbags can also be used. [Winner et al., 2015]

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Figure 2.3: Structure of vehicle safety [Leschke and Weinert, 2017]

After the accident (PostCrash) This sub-area contains measures that can originate from the infrastructure sector, such as the organisation of a suitable rescue chain, but also measures within the vehicle, such as an emergency call system, the creation of a rescue-friendly vehicle by switching on the warning and interior light or switching off fuel pumps to reduce the risk of fire. If the facts are considered from another point of view, such as that in [Leschke and Weinert, 2017], where the focus is exclusively on the vehicle with its functions and components, three concepts can be distinguished: active safety, passive safety and integral safety. The term "passive safety" describes all functions and components that serve to minimise the consequences of an unavoidable accident. All these systems are only used after the t0 , i.e. the beginning of the crash. Here, above all, all restraint systems, but also the vehicle structure and the design of crash-safe vehicle interiors can be found. On the other hand, there is "active safety" with its functions and components which serve to prevent accidents or at least reduce their severity. Here, systems such as the brake or the ESC are to be found above all. In addition, since Honda launched the first anticipatory radar-based emergency braking system in 2003, a host of other driver assistance functions have been available to assist the driver in critical driving situations. [Moritz, 2000; Justen and Baumann, 1997] In recent years, it has been recognised that the consistent

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merging of active and passive safety can open up essential, as yet untapped potentials in reducing the consequences of accidents. The term "integral safety" has become established for this interaction between active and passive safety. (Figure 2.3) Examples include the functions that are known at Volkswagen as Proactive Occupant Protection. Here, active safety sensors such as radars are used to control classic passive safety components such as the seat belt in the event of a probable crash. In addition to its pyrotechnically irreversible triggering, the seat belt has been enhanced to include a reversible electric motor to prepare the occupant as optimally as possible for an accident by reducing the seat belt slack and by connecting the occupant to the seat earlier. [Gonter and Leschke, 2016; Braess, 1996]

2.3 Development of Passive Vehicle Safety over Time Shortly after the start of the spread of the first passenger cars in Europe, the first efforts were made to actively prevent accidents. Although the spread of this new means of transport was still extremely small, it became clear early on that danger could be associated with it. Almost ten years after Karl Friedrich Benz filed his patent number 37,435 "Vehicle with gas engine drive" in 1886 [Benz, 1886], the death of Bridget Driscoll in London was the first recorded case of a traffic fatality involving an automobile [McFarlane, 2010]. In the following years, extensive technical measures were developed to further reduce the risk for vehicle occupants and other road users [Wilfert, 1973]. 2.3.1 Vehicle Structure and Restraint Systems At the beginning of the 20th century, the first technical measures to improve road safety were introduced, such as tyres with treads or mechanically driven windscreen wipers. The first patent for a seat belt also dates back to these early days of automotive engineering. The first patent on a four-point seat belt was granted to Gustave-Désiré Leveau with a French patent (No. 331,926) on 11 May 1903 [Leveau, 1903]. However, the first seat belt was not used for the first time in a production vehicle until much later, in 1959. Essential fundamental work on vehicle safety was carried out in the 1950s. Among others, the Mercedes-Benz designer Béla Barényi performed pioneering work

2.3 Development of Passive Vehicle Safety over Time

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Figure 2.4: Patent "The principle of the rigid driver’s compartment" [Barényi, 1952]

with his fundamental patent "The principle of the rigid driver’s compartment" (Figure 2.4) from 1952 [Barényi, 1952]. Building on this, the Mercedes-Benz Type 180 used a body design with crumple zones in the front and rear areas for the first time in 1953 [Baumann and Grösch, 1990]. In 1957, a special restraint system was used for the first time in passenger cars: the seat belt for drivers (Figure 2.5). At the beginning, seat belts were only used on the front seats and were pure lap belts which held the body in the seat in the lap area. The upper body was not held in the seat and was therefore not protected against a frontal collision. One year later, Nils Bohlin patented the first three-point seat belt [Bohlin, 1958], which was installed as a standard in Volvo vehicles for the first time in 1959. With the three-point seat belt, the entire upper body is held back. In the beginning, these belts were still "static" and did not adapt to the body. In the following years, the belt system was supplemented with an automatic retractor which blocked when a defined vehicle deceleration was reached, thus achieving an effective occupant restraint effect. The belt system was supplemented by headrests which were first used in 1968. The neck section was thus protected against

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Figure 2.5: Development of seat belt technology [Volkswagen Aktiengesellschaft Academy Sales & Service, 2013c]

overstretching during the backward movement of the occupants following a crash and in a rear-end collision. The introduction of crash tests in 1959 was a major milestone in improving vehicle safety. Mercedes-Benz, closely followed by Volkswagen, was a pioneer in establishing this important tool for the holistic development of safe vehicles. For the first time, it was possible to systematically and reproducibly test and improve the interplay between the body structure and the individual safety components. In 1980, a pyrotechnic belt pretensioner was introduced for the first time. It tightened the belt on impact and thus ensured a tight fit of the belt to the body. This enabled the occupant to be coupled to the vehicle deceleration at an early stage. In addition, the system was supplemented with belt force limiters (belt loops, torsion limiters). When a specified belt force level was exceeded, the belt force acting on the occupants was thus limited to biomechanically compatible values. To this day, the seat belt is regarded as the No.1 life saver in road traffic. [Schuh, 2011] At the same time as the pyrotechnic belt tensioner, airbags were offered for the first time on the driver’s side in series production vehicles in 1981. The basis for this technology was Walter Linderer’s patent, granted in 1951, for a "device for the protection of persons in vehicles against injuries in the event of collisions" [Linderer, 1951]. The idea of these patent specifications was to ensure impact protection for an occupant by means of an independently inflatable air cushion. During the 1980s, with the introduction of the passenger airbag, the protection potential for the driver was extended to the passenger.

2.3 Development of Passive Vehicle Safety over Time

21

Due to the benefits of airbag technology in accidents, which had been confirmed in tests and by accident research [Klanner, 2004], it was obvious to deal intensively with side collisions after having achieved greater protection in frontal collisions. Due to the geometric structure of passenger cars, hardly any crumple zones for energy absorption can be implemented in the side area. In addition to improving structural strength and incorporating deformation elements made of foam or plastic in the side and door panels, an attempt was made to transfer the airbag idea, which had been tried and tested in the field, to the side. In contrast to the front airbag, which was designed in combination with the seat belt to prevent the occupant from hitting the steering wheel or instrument panel, the side airbag was designed to prevent injuries resulting from an intruding side structure. The first use in a production vehicle took place in 1994 in a Volvo 850. In the following years the airbag technology was extended to further areas of application in the vehicle. In 1996, the first knee airbag was installed in the Kia Sportage. In 1998, the first side airbag which was optimised for head protection and covered the window areas up to the door sill like a curtain was available to protect both the front and rear occupants. In 2012, the pedestrian airbag in the Volvo V40 was the first airbag to be used on the outside of the vehicle. Unlike the other airbags, its purpose was to protect other road users such as pedestrians.

2.3.2 Electronics and Sensors In the early days of vehicle safety, purely mechanical systems dominated, but with the introduction of the first airbags and pyrotechnic belt tensioners, the first systems for frontal crash detection became necessary. At the beginning of the development mainly mechanical sensors were used. Examples are the Ballin-Tube sensors [Breed, 1976] as shown in Figure 2.6 and the Rolamite sensors [Bell, 1972], which were very widespread in the 1980s. The functionality of the Ball-in-Tube sensor is based on the movement of a ball in a tube. Under normal driving or braking conditions and even during light crashes, the holding forces of the magnet are sufficient to prevent the ball from moving. In more severe crashes, the ball moves through the tube towards the contacts and, after touching them, closes a circuit that triggers the airbag. Another sensor frequently used until the 1990s was the so-called Hamlin switch [Reneau, 1993], a sensor based on a spring/mass system. With the

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Chapter 2 Basics of Traffic Safety

Figure 2.6: "Ball-in-Tube" sensor [Breed, 1976]

exception of a few vehicles from the early days of sensor technology, all vehicle manufacturers used systems consisting of at least two independent sensors for reasons of robustness, above all to avoid unwanted triggering due to technical faults. At least one sensor was responsible for crash detection (discrimination sensor) and a second independent sensor confirmed this decision (safety sensor). This basic concept in the architecture of an airbag electronic system has not changed significantly to this day. The aforementioned sensors were installed in the vehicle in a wide variety of constellations. A distinction was made here essentially between decentralised approaches, i.e. different sensors were distributed in the vehicle, or a central sensor concept. Here the sensor was located centrally at one point in the vehicle. Common to all systems, however, was the approach of making a decision on the triggering of restraint systems based on vehicle deceleration. In the early 1990s, mechanical sensors were increasingly replaced by electrical micromechanical sensors [Brede and et. al., 1975]. While purely mechanical systems could only provide one switching threshold due to their physical design, the great advantage of electronic sensors was their flexibility in measuring the entire vehicle deceleration curve and making the signals thus determined available to the algorithms in the airbag control unit for further processing. With the introduction of the side airbags in 1994, the sensor concepts previously only used for front crash detection were also used for side crash sensing.

3 Vehicle Structure, Restraint and Electronic Systems The following chapter describes the state-of-the-art for all components involved in passive safety, such as the occupant cell, the vehicle structure and the various restraint systems. In particular, the electronic components such as the control unit and sensors will be discussed and the functionality of current airbag algorithms will be explained.

3.1 Vehicle Structure The vehicle structure in modern passenger cars must perform two tasks within the framework of its function as an essential component of passive safety. On the one hand, a passenger compartment with a high degree of rigidity must be ensured. This is the only way to preserve the survival space for the occupants in a serious accident. In addition, the passenger compartment must also be designed to provide crash-proof attachment points for components such as seat belts and seats. [Volz et al., 2006] On the other hand, the deformation areas must be rather flexible in order to dissipate the kinetic energy introduced by the impact in the event of a crash by means of a targeted deformation that is as compatible as possible with the occupant. For this purpose, structural measures must be taken to create a deceleration characteristic that is as compatible as possible with the occupants. These crumple zones are an essential component of an occupantload-optimised protection system. [Kramer, 2009] Figure 3.1 shows the body structure of a modern passenger car. Since the requirements on fuel consumption limit the permitted additional amount of vehicle weight, the necessary requirements are fulfilled by a clever structure made of high-strength steels. In frontal collisions, the energies that are input in the front area are dissipated via a precisely coordinated chain of deformation elements that consists of side members, centre tunnel, sills and roof frames. © Springer Fachmedien Wiesbaden GmbH, part of Springer Nature 2020 A. Leschke, Algorithm Concept for Crash Detection in Passenger Cars, https://doi.org/10.1007/978-3-658-29392-5_3

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Figure 3.1: Body structure [Volkswagen Aktiengesellschaft Academy Sales & Service, 2013b]

In the case of side collisions, the vehicle geometry means that a crumple zone is virtually impossible to implement. Here, structural options are essentially limited to the creation of a stable passenger cell. This requires the implementation of reinforced sills, optimised floor structures and crash-optimised door hinges and locks.

3.2 Vehicle Interior The entire vehicle interior is subject to special requirements in order to minimise the risk of injury in the event of an accident. For example, in a possible head impact area, a component radius of 2.5 mm to 3.2 mm must not be undershot. The steering columns with the airbag steering wheels are a particular focus. In combination with the seat belt and seat, they are an essential element for occupant protection. To ensure that the steering column and steering wheel do not penetrate too far into the interior, there are requirements that limit the penetration of the centre of the steering wheel in the horizontal and vertical directions, even in the case of accidents at higher speeds. The defined movement of the steering gear allows the steering column to be pushed together telescopically. Likewise, the foot lever mechanism must not penetrate too far into the interior during a frontal collision and must not splinter with sharp

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edges. All other vehicle interior parts also have something to do directly or indirectly with vehicle safety. The design of the inside of the door is particularly important for side protection, as it can help to reduce the load on the occupants.

3.3 Restraint Systems With regard to restraint systems, a distinction is made between devices that must be activated to benefit from their protective function (seat belts or child restraint systems are good examples) and devices whose protective function is activated automatically in the event of an accident without the occupants being involved, e.g. seat belt pretensioners, seat belt force limiters and airbags (Figure 3.2). [Gonter and Leschke, 2016] Modern seat belt systems, especially when used in combination with the airbag, are extremely powerful and ensure effective occupant protection in the event of a collision. A decisive criterion for the quality and effectiveness of a restraint system is the coordination of the individual components, i.e. only the perfect interplay of vehicle structure, steering wheel movement, seat behaviour, interior design, saet belt characteristics and airbag behaviour ensures optimum occupant protection. [Gonter and Leschke, 2016] 3.3.1 Seat Belts The three-point seat belt with automatic retraction has become established worldwide. The three-point seat belt is now fitted to almost all vehicles in all seats. In the case of adjustable seats, the lock is attached directly to the seat, while the outer side of the belt is attached to the B or C pillar. The permissible area of the seat belt anchorage points is exactly prescribed by legal regulations. The top point on the outside of the belt of many vehicles can be adjusted in height. Two independent mechanical systems ensure that the seat belts are locked in the event of an accident. One system uses vehicle acceleration or deceleration (pendulum principle). A pulse of more than 0.7 g triggers the lock. A second lock reacts to the seat belt pull-out acceleration. If a specified value is exceeded,

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Figure 3.2: Restraint systems and electronic systems of current passenger cars [Volkswagen Aktiengesellschaft Academy Sales & Service, 2013c]

then the seat belt is locked, i.e. at pull-out accelerations of more than 1 g the stop occurs within 20 mm of the pull-out length. The use of pyrotechnic seat belt pretensioners and, in some cases, additional force limiters has become established for optimum interaction of seat belts and vehicle structure. [Gonter and Leschke, 2016] Figure 3.3 shows a version of a pyrotechnic seat belt pretensioner. The pyrotechnic seat belt tensioner tightens the belt webbing depending on the severity of the accident. Within 20 ms, the seat belt is dynamically pretensioned to 2 kN in the event of a correspondingly serious accident. The illustration shows a ball seat belt pretensioner. When triggered in a crash, the firing of a pyrotechnic propellant charge ensures that the balls in the storage tube are driven by a gear wheel. The gear wheel is directly connected to the belt capstan onto which the belt is wound. The belt is therefore wound up by the movement of the gear wheel. In order to prevent the load from the seat belt on the occupant’s chest from becoming too great during this tightening process, additional belt force limiters are used which limit the tightening process when a defined force is reached. Figure 3.4 shows the operating principle of a torsional force limiter. The tractive force of the seat belt is limited by a torsion shaft in the belt capstan. Depending on the tractive force of the seat belt, the torsion shaft is more or less twisted

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Figure 3.3: Operating principle of a pyrotechnic seat belt pretensioner [Volkswagen Aktiengesellschaft Academy Sales & Service, 2013c]

Figure 3.4: Operating principle of a seat belt force limiter [Volkswagen Aktiengesellschaft Academy Sales & Service, 2013c]

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Figure 3.5: Operating principle of airbag deployment [Volkswagen Aktiengesellschaft Academy Sales & Service, 2013c]

and thus reduces load peaks. [Volkswagen Aktiengesellschaft Academy Sales & Service, 2013c] The seat belt is the only restraint system that offers a high protection potential in all accident situations. Its use alone has reduced the number of fatal accidents by more than 50%. [Rinn, 1991] 3.3.2 Airbags In addition to the seat belt system the overall restraint system is supplemented by various airbags and the risk of injury to the occupants is further optimised. It is the coordinated overall system that provides the optimum protective effect. In the case of a frontal crash, the airbags play a major role in adapting the occupants to the vehicle deceleration with the lowest possible load values. At the same time, they prevent an impact on hard vehicle components such as the steering wheel or instrument panel. In a crash, the airbag is ignited pyrotechnically. Typically, this ignition leads to the combustion of a pyrotechnic material. As shown in Figure 3.5 the airbag is filled with the generated gas. Inflating times for a driver airbag are in the range of approximately 30 ms. The resulting internal pressure of the airbag is between 1.3 bar and 1.8 bar. The force-displacement behaviour in the immersion phase can be influenced via openings in the airbag that are referred to as blow-off openings. The restraint effect can be further optimised by using adaptive airbags. The airbags can be

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Figure 3.6: Passenger car airbag system [Volkswagen Aktiengesellschaft Academy Sales & Service, 2013c]

optimised depending on the accident situation and the severity and size of the occupants. For example, the airbags can be filled to different degrees of hardness in several stages or the blow-out behaviour can be adapted. In addition to the front airbags, airbags are also used for side and oblique crashes. Side airbags protect the lower torso. Head protection is supplemented by head airbags. With side airbags, the main focus is on protection against intruding vehicle parts (Figure 3.6). [Volkswagen Aktiengesellschaft Academy Sales & Service, 2013c]

3.4 Electronic Systems for Crash Detection To reduce the acceleration load on vehicle occupants which results from a collision, the restraint systems described in the previous chapters must be used at the correct time. In the vehicle, the algorithms implemented in the airbag control unit cyclically evaluate the sensor signals generated in the crash and decide shortly after the collision has occurred whether it is necessary to fire a restraint system. At the same time, the correct time for controlling the pyrotechnic units is determined.

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The timely release of the seat belt pretensioners and airbags is intended to minimise the load on the occupants. For this purpose, it is necessary to couple the occupants to the passenger compartment movement in the event of sudden vehicle decelerations such as those typical for a crash. In doing so, the load on the stressed parts of the human body must be minimised to a tolerable extent. To minimise the load, the deceleration of the occupant that is achieved by the triggered restraint systems must be such that it begins as early as possible after the collision has occurred and is uniformly adapted to the vehicle speed by making use of the entire available forward displacement path. For the development of crash algorithms, it is therefore important to detect the need for a triggering of restraint systems and the expected crash severity as early as possible. In an ideal case, the restraining effect of the restraint system stages should neither be too low nor too high over the duration of the crash, as otherwise occupant contact with hard interior structural elements can occur on the one hand or the optimum effect cannot be developed on the other hand due to a non-optimal interaction of seat belt and airbag. The airbag control unit and its remote elements, such as the various sensors, are highly interlinked, as are all other control units in the vehicle. Due to the fast data transmission rates, the necessary reliability and the required function in a crash - even after the on-board power supply has collapsed - all remote airbag sensors are directly connected to the airbag control unit. Digital communication is often carried out via a special protocol called Peripheral Sensor Interface 5 (PSI5) interfaces. Communication with other relevant control units is via an overall-vehicle bus system, usually a Controller Area Network (CAN bus) [Burg and Moser, 2017]. The restraint systems, such as seat belt pretensioners, seat belt force limiters and airbags, are also directly connected to the airbag control unit via cables. They are triggered by the airbag control unit switching on a defined current pulse.

3.4.1 Airbag Control Unit The airbag control unit is the heart of the restraint system. Special algorithms are used here to make the decisions for triggering the various restraint systems in a crash. At the same time, the entire system is permanently checked for correct functioning from here. If faults occur, they are indicated by the airbag

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warning lamp in the instrument cluster. This prompts the customer to have the vehicle repaired. In addition to triggering the restraint systems, the airbag control unit initiates further measures to protect the occupants. For example, the fuel pump is switched off and the hazard warning lights are activated. [Kramer, 2013] Airbag electronics essentially have the following main tasks: • Crash detection (front, side, rear, rollover) and defined triggering of seat belt pretensioners, airbags and initiation of further measures such as battery interruption • Permanent monitoring of the entire airbag system and storage of fault and crash data • Activation of the airbag indicator light in the event of system failure and notification of a crash event to other system components via CAN bus • Storage of faults in the error memory • Self-sufficient energy supply of the function in the crash by means of energy reserve Due to their critical functions, airbag control units are subject to particular challenges when it comes to reliability, fault tolerance and failure probabilities. In addition to fulfilling ISO 26262 on functional safety [ISO, 2011] hardware and software implementations that rely on continuous redundancy of the two-paths have therefore become established. Figure 3.7 shows this in simplified form. Triggering decisions are always determined on the basis of at least two sensors. Calculation is always performed by at least two processors with independent algorithms. [Reif, 2014] This concept of triggering algorithms, plausibility and saving forms the basis for the safe triggering of restraint systems. In the further consideration of crash algorithms, only the complex trigger path is considered. This is the focus of the algorithm. The other algorithms in the other paths are much simpler and can therefore be neglected. [Kramer, 2013] A typical technical implementation of this concept is shown in Figure 3.8 below.

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Figure 3.7: Redundancy in the airbag control unit

3.4.2 Crash Sensors The sensors installed in the airbag control unit are the core of the crash sensor system. They are located on the circuit board of the control unit and record the vehicle deceleration in x (direction of travel), y (side) and z (rollover) direction. Due to the extremely high requirements on the firing times (less than 10 ms) for the restraint systems for side protection, additional external sensors are used which very quickly record the vehicle deceleration in a side crash and make it available to the algorithm in the airbag control unit via a direct PSI5 interface. As a rule, these are acceleration sensors in the area of the b and/or c pilars. In most vehicles, pressure sensors are also installed in the front doors. They measure a differential pressure between the air pressure in a sealed cavity and the ambient pressure. In a crash, pressure fluctuations occur in cavities, such as in the front doors, as a result of the pressure of the outer door panel. A significant increase in the differential pressure thus indicates a deformation of the structures surrounding the sensor. From a physical point of view, the pressure measurement is significantly faster than the measurement of deceleration signals. Therefore, the principle of pressure measurement allows a further reduction of the firing times for the time-critical lateral restraint systems. Due to the significantly increased requirements on frontal crash detection in recent years, many vehicles are equipped with additional acceleration sensors in the front end area. These sensors provide characteristic signals in certain

3.4 Electronic Systems for Crash Detection

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Figure 3.8: Airbag control unit

accident constellations and improve the separability of the different crash types. [Reif, 2012] Figure 3.9 shows such a sensor system. Micromechanical acceleration sensors have been used in airbag deployment systems since the early 1990s. In highly integrated electrical circuits, they combine both the actual sensor element and the signal conditioning, the analogue/digital converters and the digital interfaces for communication with the processing electronics. [Ahlers, 1997] The actual acceleration measurement is based on the displacement of a moving seismic mass as a result of acceleration. In addition to sensor elements based on the principle of a bending beam, there are above all sensors that use a capacitive measuring principle. Figure 3.10 describes the operating principle of a capacitive acceleration sensor. The sensor is housed on a chip with a surface area of approx. 9 mm2 together with the analogue evaluation circuit. Approx. 1 mm2 is required for the acceleration sensor. A movable central part "floats" just above the chip surface and is connected to the chip by four bending beams. The bending beams have an edge length of approx. 2 μm and a length of approx. 200 μm. The total mass of the moving part thus weighs less than 0.1 μg. In these dimensions, silicon is very elastic, comparable to a steel construction on a larger scale. The bending beam withstands shock loads of up to 2,000 g in all axial directions without damage. At the movable middle part there is a finger structure, into which a second one,

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Figure 3.9: Sensor concept of current passenger cars

Figure 3.10: Measuring principle of capacitive acceleration sensor [Ahlers, 1997]

3.4 Electronic Systems for Crash Detection

35

which is connected with the chip, engages. Such a unit cell is shown by the dotted line in Figure 3.10. The comb structure, which consists of 42 unit cells, forms a differential capacitor consisting of C1 and C2. Without acceleration, both capacitances are equal (approx. 0.1 pF). An acceleration in only one direction, as shown in Figure 3.10, reduces C1 and increases C2. By measuring C1 and C2, the acceleration acting on the sensor can be calculated using the mass of the moving part and the spring constant of the bending beams. In order to achieve good linearity over the entire measuring range, an evaluation circuit is selected in which the current acceleration force is compensated by an electrostatic force in a closed control loop. This electrostatic force is applied by applying a DC voltage to the finger structure. The controller selects the voltage at the 42 movable electrodes (opposite the stationary ones) so that the two capacitances C1 and C2 are equal, i.e. the movable middle section returns to its rest position. The voltage at the moving electrode is then a direct measurand for the prevailing acceleration in the direction of the moving middle part. To detect the centre position of the moving part, the two capacitances C1 and C2 are determined by means of a 1 MHz carrier frequency (it is sufficient to determine only the equality of C1 and C2). Most temperature-induced nonlinearities no longer result in faulty measurement results, since the bending beams are stress-free in the steady state of the control loop. Since the sensor can move the moving middle part independently, the electronics can carry out a complete function check of the sensor element. This feature is indispensable for a sensor that is used in safety-relevant systems. The functionality of the sensor can thus be checked regularly by the airbag control unit. [Ahlers, 1997] Acceleration sensors are installed both on the circuit board of the airbag control unit (Figure 3.11), and as so-called satellite sensors at various positions in the vehicle. For this purpose, they are installed in separate housings (Figure 3.12) and are connected to the vehicle body via screw connections. The use of air pressure sensors for side crash detection, as shown in Figure 3.13, is based on a Siemens patent [Schmidt and Acht, 2004]. They are mainly used in the door area. There is a cavity between the outer and inner door panels which is deformed by the penetrating object in the event of a crash. A sensor integrated in this cavity measures the increase in pressure during compression. This essentially adiabatic process can be evaluated in correlation

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Chapter 3 Vehicle Structure, Restraint and Electronic Systems

Figure 3.11: Integration of acceleration sensors on the circuit board of the airbag control unit

Figure 3.12: External acceleration sensors

to the crash severity progress. In side collisions, the air in the deformed side part is compressed as soon as the vehicle deforms in the area of the point of impact. Similar to the acceleration sensors described in the previous chapter, a micromechanical capacitive measuring sensor is used. The measuring principle is shown in Figure 3.14. The entire sensor element consists of a large number of plate capacitors, the sensor units. Within this sensor unit, capacitor plate 1 is arranged in a sealed

Figure 3.13: External pressure sensors

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Figure 3.14: Principle image of capacitive pressure sensor with membrane structure [Volkswagen Aktiengesellschaft Academy Sales & Service, 2013c]

Figure 3.15: Signal processing from sensor to algorithm based on [Zander, 2003]

cavity. Capacitor plate 2 is stretched over it as a diaphragm. When pressure is applied to the diaphragm, distance d between the capacitor plates changes. This change is processed in the evaluation electronics and is made available as a signal for further processing. The lower cut-off frequency of the sensor element, and thus the frequency of the minimum measurable air pressure fluctuations, is determined by the structure of the silicon membrane. Due to the resulting highpass characteristic, the absolute air pressure cannot be measured. Therefore, only the significant pressure changes during the crash process are evaluated. [Kramer, 2013] 3.4.3 Signal Processing For digital use in the airbag control unit, the signal data that are recorded analogously by the sensors must be processed further in a signal processing chain. The functional principle is shown in Figure 3.15. An analogue-to-digital converter (A/D converter) discretizes a time-continuous input signal into individual discrete samples. These samples are then converted

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Chapter 3 Vehicle Structure, Restraint and Electronic Systems

into digital values. Due to a finite number of possible initial values, quantization always takes place. The result of an A/D conversion can be displayed in a signal-time diagram in a point sequence with stepped horizontal and vertical distances. The main parameters of an A/D converter are its bit depth and its maximum sampling rate. The minimum sampling frequency required for lossless discretization is determined by the bandwidth of the input signal. In order to completely reconstruct the signal later, the sampling frequency must be greater than twice the maximum possible frequency in the input signal. Otherwise, sub-sampling occurs, i.e. the reconstructed signal contains frequencies that were not present in the input signal. Therefore, the input signal must be band-limited. For this reason, the output signal of the sensor is first limited by a low-pass filter. The signal is then sampled by an A/D converter and converted into digital output values. [Werner, 2008] The sampling theorem formulated by Shannon must be observed during implementation. It states that a function which does not contain frequencies greater than fmax is clearly defined by any series of function values at a distance of τ=

1 2 · f max

(3.1)

In signal processing, this corresponds to sampling at a sampling rate of fsamp ≥ 2 · f max

(3.2)

The resulting signal representation is referred to as pulse amplitude modulation. [Meyer, 2017] For reconstruction, this signal is filtered by an ideal low-pass filter with cut-off frequency fmax [Beucher, 2018]. Typically, sampling frequencies between 1 kHz and 4 kHz are used in current airbag control units. Bessel filters of a higher order with an upper cut-off frequency of 400 Hz are generally used for the filter. This frequency range has proven to be best suited for further processing in crash algorithms. After the A/D conversion, the current discrete time and value sequence is processed further. The sensors used in the airbag control unit are designed in such a way that they are not destroyed by loads of several hundred g. At the same time, they should work as accurately and linearly as possible in their actual measuring range. In order to implement both

3.5 Airbag Algorithms

39

requirements, the sensor must be limited to its working range. For this purpose, the signals outside the measuring range are cut off. Since, after this so-called clipping, signal components can exist above the cut-off frequency according to the Shannon theorem, the signals are processed again with a 400 Hz low pass. A digital high-pass filter with a lower cut-off frequency of approx. 15 Hz is used to absorb possible zero-point shifts of the micromechanical sensor elements and their analogue signal processing hardware due to temperature and ageing effects. This filters out the equal components of the signal. The pre-processed digital acceleration signal can then be further processed in the crash algorithm. [Zander, 2003]

3.5 Airbag Algorithms The task of an airbag algorithm is to trigger the right restraint system at the right time in real accidents. Since a multitude of different accident constellations can be found in the real world, which naturally cannot all be considered individually, the algorithms are designed on the basis of supporting points, which represent reality as comprehensively as possible.

3.5.1 Legal Requirements, Consumer Test, Field Situation Based on accident analyses, a set of crash load cases has become established as a standard for all vehicle manufacturers. Many of these crash load cases are described by laws or by the requirements of consumer test organisations (e.g. European New Car Assessment Programme (EuroNCAP) or US New Car Assessment Programme (USNCAP). [Lehmann, 1994; Fails and Minton, 2001; Lie and Tingvall, 2002] Others result from a more extensive analysis of the accident situation. Another task of the airbag algorithm is to ensure that no restraint system is fired in the case of Misuse events that are similar to a crash (or that appear similar to a crash to the sensors used). For this reason, nontriggering load cases are also included in the application. [Engelmann et al., 2012] The required load cases can be divided into three different groups:

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Chapter 3 Vehicle Structure, Restraint and Electronic Systems

A: Misuse or NoFire load cases The first group shown in Table 3.1 includes nondeployment events such as low-speed crashes or Misuse attempts. Low-speed tests (e.g. by insurance associations such as Research Councils for Automobile Repairs (RCAR)) are usually carried out at speeds below 16 km/h [RCAR, 2010]. The crashes are thus below a threshold that puts significant strain on the occupants. It is therefore not necessary to trigger restraint systems. The Misuse tests include, for example, driving on bad roads, kerbing slides, driving over railway sleepers or collisions with wildlife or light obstacles such as reflector posts. The selection of Misuse events strongly depends on the sensor used. The Misuse load cases described are aimed at the acceleration sensors used in a frontal crash. In the case of the pressure sensors used in the side airbag deployment not considered here, these are load cases related to the type of measurement, such as the impact of footballs and shopping trolleys on the doors. Table 3.1: Typical set of Misuse and NoFire load cases for an airbag application NoFire and Misuse

Reason

16 km/h RCAR left, right 16 km/h RCAR 10° left, right 10 km/h RCAR Bumper left, center, right 50 km/h 60 km/h left, center, right 70 km/h left, center, right 80 km/h left, center, right 60 km/h 30 km/h Static 80 km/h 60 km/h ... 70 km/h

Low speed accidents, parking space Low speed accidents, parking space Low speed accidents, parking space Driving onto curb Collision with wildlife Collision with wildlife Collision with wildlife Cross-drain (pothole) Gravel pile Hammer blows Cobblestones Bump ... Leap

B: Load cases at the lower limit of the required restraint system release The second group is in the medium crash intensity range and marks the lower limit above which an airbag deployment must be safely achieved due to the occupant load values ([Hollowell, William T. et. al., 2018]). They are shown at the beginning of Table 3.2. The speeds here are usually between 27 km/h and 40 km/h, depending on the crash type. Just as in the following group, the crash types should be varied in such a way that they contain the accident scenarios occurring in real accident situations as comprehensively as possible.

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41

Table 3.2: Typical set of Fire load cases for an airbag application Fire load cases

Reason

27 km/h Wall 0° 30 km/h Truck 32 km/h Wall 30° left 32 km/h Wall 0° 32 km/h Wall 30° right 35 km/h Pole 40 km/h ODB left 40 km/h Wall 30° left 40 km/h Wall 0° 40 km/h Wall 30° right 40 km/h ODB right 50 km/h Wall 0° 56 km/h ODB left 56 km/h ODB right 56 km/h Wall 0° 64 km/h ODB left 64 km/h ODB right

Lower deployment limit head-to-tail collisions Lower deployment limit head-to-tail coll. involving a truck Lower deployment limit for overlap crash scenarios NAR: FMVSS208 NAR: FMVSS208 Lower deployment limit for coll. involving a tree Lower deployment limit for coll. involving two vehicles NAR: FMVSS208 NAR: FMVSS208 NAR: FMVSS208 Lower deployment limit for coll. involving two vehicles RdW: 5* EuroNCAP RdW: ECE R94 RdW: ECE R94 NAR: FMVSS208 & 5* USNCAP RdW: 5* EuroNCAP & NAR: IIHS Top Safety Pick RdW: 5* EuroNCAP & NAR: IIHS Top Safety Pick

As a rule, these are frontal accidents against barriers of varying hardness and geometric characteristics with complete or partial coverage of the opponent. In addition, other rather rare types of accidents such as underrun accidents against the rear of trucks or tree accidents should also be considered [Chan, 2000]. C: High-speed load cases The typical high-speed crashes as shown at the end of Table 3.2 form the third group. As a rule, the legally defined load cases such as a 56 km/h barrier crash ([Insurance Institute for Highway Safety, 2017])or a 64 km/h EuroNCAP crash ([EuroNCAP, 2017]) against a deformable barrier can be found here. When using a different classification method, the load cases of the second and third group can be divided into accident types with fast crash severity progress (FP), such as the wall 0° load cases against a rigid barrier, and accident types with rather slow crash severity progress (SP), such as the ODB tests against a deformable barrier or the truck underrun accidents. This classification refers to the speed at which the occupant moves forward during the accident.

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Table 3.3: Classification of load cases Load case

Load case group

Crash progress

56 km/h Wall 0° 50 km/h Wall 0° 40 km/h Wall 0° 32 km/h Wall 0° 27 km/h Wall 0°

C C C C C

fast

64 km/h ODB left 64 km/h ODB right 56 km/h ODB left 56 km/h ODB right 40 km/h ODB left 40 km/h ODB right 35 km/h Pole 40 km/h Wall 30° left 40 km/h Wall 30° right 30 km/h Truck 32 km/h Wall 30° left 32 km/h Wall 30° right

B B B B B B B B B B B B

slow

60 km/h - 80 km/h Wildlife 10 km/h - 16 km/h RCAR Hammer blows 80 km/h Cobblestones 60 km/h Bump ... 70 km/h Leap

A A A A A A A

-

If the three groups are classified according to the classification presented, the picture shown in Table 3.3 shows a complete set of load cases for a typical airbag application. The Fire load cases shown differ both in their speed and in the barriers used. In addition, the dummies used in the experiments to determine the occupant load vary, whereby the dummy type used is not relevant for the application of the algorithms. If the Fire load cases are considered, three different barrier types are essentially in use worldwide. The so-called ODB barrier, a honeycomb structure which is used with a 40% overlap. It represents the rather soft structure of another vehicle in a straight frontal collision.

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43

Figure 3.16: Different crash barriers

The second barrier type is a rigid wall with a 100% overlap relative to the vehicle. The same barrier is used at an angle of 30°. Both represent accidents with hard, inflexible structures. In addition, there are also pole and underride barriers, each of which represents a rather rare type of accident like tree accidents or collisions with a truck. All barrier types are shown in Figure 3.16. The Misuse load cases are usually examined on test sites.Figure 3.17 shows some examples.

3.5.2 Firing Requirements on Restraint Systems in Passenger Cars Restraint systems such as airbags and seat belts in the vehicle are designed to reduce the kinetic energy of the occupant as much as possible during a crash. The basic question that needs to be answered is as follows: "Which restraint system must be fired at which accident conditions and when does triggering have to take place?". A restraint system must not put additional strain on the occupants. In accidents at lower speeds, it is often possible to dispense with the deployment of a seat belt pretensioner or airbag, as the protective potential of

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Chapter 3 Vehicle Structure, Restraint and Electronic Systems

Figure 3.17: Misuse tests

the mechanical belt is sufficient. In heavier crashes on the other hand, optimum protection can only be achieved through the interaction of seat belt pretensioners and airbags. In simplified terms, occupant protection is required in accidents with a speed difference of more than approx. 27 km/h. Here it is important to note that the speed value in question refers to the speed difference and not to the absolute speed of the vehicle. When a vehicle that travels at a speed of 27 km/h collides with a rigid wall, the change in speed is 27 km/h. On the other hand, in the event of a rear-end collision between two vehicles of the same weight that travel one behind the other with the first vehicle traveling at 100 km/h and the second vehicle traveling at 127 km/h, the change in speed is also Δv of 27 km/h. Two vehicles approaching each other at 27 km/h generate a Δv of 54 km/h. Just as much as a vehicle that collides with another stationary vehicle at 54 km/h. Due to the large number of accident constellations with different speeds and hit positions, it is not possible to design the system using only a vehicle speed threshold. The difficulty lies in determining the correct triggering time for the airbag algorithm from the signals determined in the accident. The correct triggering time for a front airbag is one of the essential criteria in restraint system

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45

design. If the airbag opens too early, the restraining effect is no longer given when the head hits the airbag. Due to the short service life of approx. 70 ms of these systems, the required internal pressure is then no longer available. If the airbag opens too late, the occupant may be hit by the airbag which would result in an unnecessary strain on the occupant. [Seiffert, 1992] A simple rule that has been used for many years to determine the required firing time in frontal crashes is the "5 inch minus 30 ms" rule. This means that the required algorithm trigger time results from the time in which the head of the occupant moves forward by 5 inches, minus 30 ms. The rule results from the two boundary conditions described below: • The expected distance between a fully deployed airbag and the occupant’s head at the start of the crash in its original sitting position is approx. 5 inches • The typical time between the triggering of a driver airbag and its full deployment is approx. 30 ms. This approach is simplistic because both the distance between the head and the deployed airbag and the inflation time can be influenced by various factors. Nevertheless, it is used as a sufficient design parameter for most crash applications. Although, for example, occupants of different sizes or with different seat positions result in different distances to the deployed airbag, the lack of more precise systems for monitoring occupant displacement currently prevents a more precise differentiation. Only at 30 ms are adjustments to the rule to be made, since, for example, current more powerful gas generators in driver airbags can inflate the airbag much faster. The firing times for passenger airbags may also have to be adjusted. These usually have a larger volume, so that the inflation time is longer with the same output of the gas generator. If equal deployment times are desired for both front airbags, stronger generators may have to be used for the passenger airbag. [Chan, 2000] Typical triggering times for restraint systems for the various load case types are shown below: Wall 0° barrier ODB barrier Wall 30° barrier Pole, Truck barrier

10 ms – 20 ms 25 ms – 45 ms 30 ms – 40 ms 40 ms – 50 ms

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Chapter 3 Vehicle Structure, Restraint and Electronic Systems

3.5.3 Crash Detection Basics Crash detection must be based on reproducible, measurable signals or observed vehicle behaviour with a correlation to the crash severity. In the algorithms on the basis of acceleration signals that are currently used, these are, for example, measurands such as deceleration and the values calculated from it such as speed changes, jerk or energy reduction. Nearly all algorithms used today for crash detection are based on thresholds, i.e. if one or more defined thresholds are exceeded, the restraint systems are triggered. [Chan, 2000] Of course, methods of machine learning such as neural networks with deep-learning algorithms or fuzzy logic-based approaches are conceivable, but are currently not used [Henne et al., 2002; Basir and Karray, 2005]. In principle, algorithms that are based solely on the evaluation of deceleration signals, i.e. on the direct measured value provided by today’s sensors, would be conceivable. On closer inspection, however, it can quickly be seen that this criterion alone is not sufficient to meet the requirements on crash detection. Even though the average deceleration amplitude usually increases with the crash severity, a measurement of deceleration peaks or jumps is not sufficient to definitely predict a severe crash. The reason for this is that other events such as hammer blows, bad road stretches or similar Misuse events can also produce extremely high deceleration peaks over short periods of time. In addition, it is hardly possible to deduce the required triggering times of the restraint systems from the time of occurrence of these deceleration peaks. For this reason, the measured deceleration signals are processed accordingly. In most known algorithms, the speed reduction of the vehicle as integral of the deceleration is the leading application parameter. [Chan, 2000] A large short-term speed reduction always correlates with a crash event. As described, for example, in the patent "An Acceleration sensor Arrangement" [Olsson, 1994], entire vehicle decelerations are usually determined via an acceleration sensor, integrated into a speed reduction signal and then fed to an evaluation algorithm. In the algorithm, the speed reduction is then observed in a previously defined time interval and an fire command is given to the downstream control unit after a defined threshold has been exceeded. The application of such an algorithm is described in the following chapter. In recent years, the number of crash load cases has grown exponentially, mainly following pressure from consumer test organisations. For all these load cases,

3.5 Airbag Algorithms

47

an optimal firing time must be found in the algorithm and the separation from the Misuse load cases must be ensured. This is no longer possible on the basis of a simple algorithm designed only for crossing a single threshold. [Feser et al., 2004] In addition to the evaluation of vehicle deceleration, current algorithms therefore also use other variables for crash discrimination. This additional information can be divided into two different groups: • The evaluation of further measurands, which are derived mathematically from the vehicle deceleration. The vehicle decelerations are still based on the measurement of the same sensor. • The use of additional sensors. A distinction must be made here between sensors in the airbag control unit, and thus in the non-deformed area of the vehicle, and sensors outside the control unit, which can be distributed at different positions in the vehicle. In the first group there are algorithms that calculate not only the vehicle deceleration but also further variables from the measured vehicle deceleration. Possible here are, for example, the jerk (1st derivative), the velocity (single integral), the path or the deformation (double integral) or other parameters such as the energy (function of deceleration and velocity) that are calculated from various data. In automotive applications of the various electronics suppliers and vehicle manufacturers, a large number of evaluation logics can be found, which basically meet all of the following criteria: • Real-time sensing is necessary for timely triggering during a crash. • Crash algorithms must reproduce the same results from the same sensor signals. An increase in the measured values must be proportional to the severity of the crash, discontinuities are not permissible. • Triggering decisions must be made extremely quickly and with foresight, as the inflation time of the airbags must be taken into account. A fast 56 km/h frontal crash results in a time window of 40 ms between the beginning of the accident and the ideal time for the occupant’s head to come into contact with the airbag. With an airbag inflation time of 30 ms, only 10 ms remain for the evaluation of the measurement signals in the airbag control unit. This means

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that the crash algorithm can only use a narrow time window of signals to predict the crash load and severity. • Crash algorithms usually use more information from the crash signal than just the pure change of the vehicle speed. The increased requirements that have resulted from consumer tests in recent years in particular are forcing developers to differentiate more and more between crash load cases. A solution frequently used in current vehicles (as described, for example, in patent DE 195 81 772 B4 [Kozyref, 2004]) uses an additional deceleration sensor placed in the front end of the vehicle to improve the separation capability for accident situations which overlap in the short-term signal curve that is available for evaluation. The position is selected so that it shows a high deceleration for an ODB test and a low deceleration for an RCAR test. On the basis of this additional information, the trigger threshold is influenced in order to be able to separate the two mentioned load cases from each other. Another methodology to be found in current vehicles uses a sensor built into the airbag control unit in vehicle y-direction. The actual function of the sensor is to detect side crashes. But significant signals can also be found on the y-sensor in frontal crash events - especially in those with a high oblique share, such as the 40 km/h 30° Wall crash. In 0° load cases, such as the 56 km/h Wall 0° load case, there is almost no signal on the y-sensor. The differentiating information available in this way can then also be used to influence the threshold. Due to the increased use of environmental sensors in current vehicle projects, the use of pre-crash signals is increasingly making its way into algorithms. In the patent "Method and control device for triggering passenger protection means for a vehicle" [Jace, 1992] describes, for example, a procedure to use signals from foresighted sensors (e.g. a radar) to influence the triggering decision of the algorithm (Figure 3.18). The radar measures the relative distance between the ego vehicle and the opponent and calculates the probability of an accident. On the basis of this information, the vehicle deceleration-based algorithm, which continues to be used as the main decision maker, is supported in the triggering decision. For example, if a reliable distinction is made between a subsequent-vehicle collision or a collision with a wildlife, the corresponding algorithm thresholds can be switched less sensitively. This enables faster triggering times with greater robustness. These pre-crash approaches for algorithm improvement are increasingly found in current vehicles on the European market. The predictive sensors that are used in vehicles primarily due to the requirements of consumer test institutes in the

3.5 Airbag Algorithms

49

Figure 3.18: Method and control device for triggering passenger protection means for a vehicle [Jace, 1992]

area of active safety, in particular emergency braking, are thus used in the area of passive safety. In addition to these algorithm concepts (which correspond to the current stateof-the-art), various papers and patents are available in the literature which relate to the improvement of algorithms on the basis of other parameters calculated from vehicle deceleration. The aim of all these approaches is to evaluate further variables in order to find characteristic features that differentiate the various load cases either in principle or earlier in the progress of the crash. These procedures make it possible to make firing decisions earlier in the progress of a crash or to separate Misuse load cases from Fire load cases in a more stable way. Below are some algorithm approaches and the measurement variables used in them. In his concept "A predictive based algorithm for actuation of an airbag" T. Gioutsos proposes the use of the jerk as the 1st derivative of acceleration as the used measurand [Gioutsos, 1992]. In [Watanabe and Umezawa, 1993; Watanabe and Umerawa, 1993; Watanabe et al., 1994], Watanabe and Umezawa refer to an algorithm that predicts the forward displacement of the occupant in the crash with the help of the calculated variables acceleration, jerk and energy and makes an firing decision based on this. In their paper "SIR deployment method based on occupant displacement using velocity enhanced acceleration crash metrics", Cashier and Kelley follow a similar approach based on the same measurands

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Chapter 3 Vehicle Structure, Restraint and Electronic Systems

Figure 3.19: US Patent 5.040.118 R. Diller [Diller, 1991a]

[Cashler and Kelley, 1995]. In his patent "Method for controlling the release of passenger restraint systems", B. Mattes shows a threshold-based algorithm based on the calculation of speed reduction at variable thresholds [Mattes and et. al., 1991]. In their patent "Control unit for a passenger restraint system and / or passenger protection system for vehicles", Eigler and Weber demonstrate a method for making a decision on the triggering of restraint systems in parallel-running algorithms via several thresholds, each specialized for one load case [Eigler and Weber, 1993]. Tobaru, Blackburn and Gentry show similar approaches in their patents which make the firing decision on the basis of energy and acceleration signals in different frequency bands [Tohbaru, 1993; Blackburn and et. al., 1991; Blackburn and Gentry, 1992]. Two other examples of variants of algorithms based on a total vehicle deceleration are described in more detail below. In US patent 5.040.118, R. Diller proposes the use of a divided overall algorithm. Each of these algorithms shown in Figure 3.19, which are calculated in parallel, is based on a different measured quantity calculated from the total vehicle deceleration. Exemplarily, a primary algorithm runs on the basis of a vehicle deceleration and two further algorithms run on the basis of the forward displacement and the jerk [Diller, 1991a,b].

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51

All three algorithms make an independent Fire or NoFire decision. This decision is linked to an algorithm function that evaluates whether one of the three algorithms mentioned here recognizes on the basis of the signal curve that it is best-suited for triggering in a timely manner in this type of accident. A score is calculated from the three fire decisions, which triggers the restraint systems when a defined threshold value is exceeded. [Diller, 1991a,b] In SAE Paper 920478 [Jace, 1992] Allen Jace suggests an algorithm which combines the values jerk (j) and speed reduction (v) which were calculated based on the deceleration (a) via two additively calculated functional parts. The combination of these two measurands is carried out by calculating a socalled power rate as the first derivative of the power, which results as follows: Speed: 

t

v(t) =

a(t)d t

(3.3)

0

Jerk: j(t) =

d a(t) dt

(3.4)

The algorithm physically considers the conversion of the power in the crash via the kinetic energy of the crash pulse. Energy: E(t) =

1 m · v(t)2 2

(3.5)

d E(t) dt

(3.6)

Power: P(t) =

P(t) = m · v(t) · a(t)

(3.7)

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Chapter 3 Vehicle Structure, Restraint and Electronic Systems

Power Rate: d P(t) =

d P(t) dt

(3.8)

d P(t) = v(t) · j(t) + a(t)2 With:

(3.9)

m=1

Jace shows in the chronological sequence in the early phase that the first term, consisting of velocity reduction and jerk, is the dominant variable within the first 40 ms and thus in the relevant time window for an airbag deployment. The combination of both pieces of information shows clear advantages, since both terms can balance out their advantages and disadvantages in different crash and near-crash situations. With crash types such as low-speed, oblique and pole crashes, for example, the large jerk can compensate for the rather small velocity reduction in the initial phase. With regard to all requirements, this algorithm improves the performance compared to an approach that is purely based on velocity reduction. [Jace, 1992] All these approaches have in common that they use the total vehicle deceleration and measurands derived from it. However, the basic problem of this measurement signal remains the same for all approaches. The information content of the total vehicle deceleration is naturally limited, therefore none of the shown proposals contains an approach how e.g. direction information can be determined.

3.5.4 Crash Algorithm Based on the Velocity Reduction of the Overall Vehicle As shown in the previous chapters, crash algorithms essentially have to fulfil two tasks. The main task is to evaluate a situation on the basis of the measured variables used or the further information calculated from them and to decide whether there is a crash that requires the triggering of a restraint system. The second main task, is to determine the correct triggering time. As a rule, only the same measured variables are available for this purpose as well. Using the example of a simple algorithm based on the pure observation of

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53

the vehicle deceleration, as it was in use until approx. 2000, the following is shown. The aim is to determine a threshold that enables both of the following: All load cases that do not require a restraint system are to be placed below a threshold in a timely manner. At the same time, all load cases that definitely require a release are to exceed the threshold in time for a correct firing decision to be made. The possibilities to influence the application are therefore rather limited. Influencing the threshold is the only degree of freedom available to influence the result that the algorithm generates from the input variables. With the acceleration sensors presented in Section 3.4.2, signals are permanently recorded during driving. For further consideration in the algorithm, the raw signals are pre-processed via a signal processing chain as shown in Section 3.4.3. In a further step the calculation of the calculation variables used in the algorithm takes place. The decision threshold is not determined in real time in the vehicle, but must be determined beforehand. For this purpose, the required firing times for all the load cases considered for the design are determined. To determine the triggering threshold, it must be determined at which point in time restraint systems such as airbags and seat belts must be triggered in order to optimally develop their protective potential. This determination must be made for all load cases considered. This is shown below for the deployment of the front airbags. Section 3.5 has already introduced the 5 inch criterion. Diagram 3.1 visualizes the basic procedure. In a real vehicle crash, the overall vehicle deceleration is determined with an acceleration sensor on the centre tunnel. Through twofold integration, the first approximation of the forward displacement of the occupant over time is calculated. If the time at which the occupant has moved by 5 inches is considered and the 30 ms are subtracted, the result is the airbag deployment time required for this load case. The firing time tfire determined in this way is determined in the same way for all load cases shown in Table 3.2. For a vehicle from the small car segment, this results in the values shown in Table 3.4. Within the scope of the application, vehicle decelerations are run in the real test for all Fire load cases and the NoFire / Misuse load cases. The deceleration signals are integrated accordingly and thus converted into a velocity reduction. If the vehicle decelerations determined from the tests for all load cases and

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Diagram 3.1: Determining the firing time of a load case from the forward displacement of the occupants Table 3.4: Vehicle-specific triggering requirements Firing Time Requirement [ms]

Crash progress

56 km/h Wall 0° 50 km/h Wall 0° 40 km/h Wall 0° 32 km/h Wall 0° 27 km/h Wall 0°

10 13 16 17 20

fast

64 km/h ODB left 64 km/h ODB right 56 km/h ODB left 56 km/h ODB right 40 km/h ODB left 40 km/h ODB right 40 km/h Wall 30° left 40 km/h Wall 30° right 32 km/h Wall 30° left 32 km/h Wall 30° right 35 km/h Pole 30 km/h Truck

28 28 35 35 41 41 32 32 39 39 41 51

slow

Load case

3.5 Airbag Algorithms

55

Misuse or NoFire requirements are shown in a diagram, the set of curves shown in Diagram 3.2 is generated. In Diagram 3.2 and in the diagrams shown in the following chapters, colours are assigned to the curves of the various load cases according to their group affiliation. Table 3.5 shows the colour assignment valid for the entire work. Table 3.5: Assignment of colours to the individual load case groups Load case

Abbr.

Load case group

56 km/h Wall 0° 50 km/h Wall 0° 40 km/h Wall 0° 32 km/h Wall 0° 27 km/h Wall 0°

56W 50W 40W 32W 27W

Wall 0°

64 km/h ODB left 64 km/h ODB right 56 km/h ODB left 56 km/h ODB right 40 km/h ODB left 40 km/h ODB right

64Ol 64Or 56Ol 56Or 40Ol 40Or

ODB

40 km/h Wall 30° left 40 km/h Wall 30° right 32 km/h Wall 30° left 32 km/h Wall 30° right

40Wl 49Wr 32Wl 32Wr

Wall 30°

35 km/h Pole 30 km/h Truck

35P 30T

60 km/h - 80 km/h Wildlife 10 km/h - 16 km/h RCAR Hammer blows 80 km/h Cobblestones 60 km/h Bump ... 70 km/h Leap

Colour

Other

NoFire/Misuse

In the following, two algorithm concepts that are used very frequently in vehicles are presented. They are used in the vast majority of passenger cars worldwide and thus reflect the state-of-the-art. The first approach is based on the concept of evaluating a single acceleration sensor which is used to determine the overall vehicle deceleration as described in [Olsson, 1994]. This algorithm is found above all in vehicles that were launched until about the year 2000. At that time, the requirements from consumer test organisations were not yet so high, i.e.

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Diagram 3.2: Velocity in various selected crash scenarios

this relatively simple approach was able to meet all requirements. Diagram 3.3 shows this graphically. It can be seen that a simple linear threshold can be defined, which is cut by all load cases shown here in such a way that a timely triggering of the restraint systems according to the requirements valid at this time is possible. The second approach is based on the concept described in [Kozyref, 2004] and shows a state-of-the-art algorithm that has been used in almost all vehicles worldwide since 2010. With the load cases added between 2000 and today (as shown in Figure 3.16), separation on the basis of a single threshold is no longer possible. This is explained in Diagram 3.4 using an example. A high-speed Fire load case with 64 km/h against an ODB barrier is compared to a rather slow 16 km/h NoFire load case against a hard wall. The latter is a typical representative of a light parking accident, as it is used by insurance institutes to classify insurance classes. In this RCAR load case, airbag deployment must be avoided at all costs. The aim of designing a threshold in this case would be to have the NoFire crash safely above the threshold and the Fire crash safely below the threshold at the

3.5 Airbag Algorithms

Diagram 3.3: Crash application acc. to Olsson

Diagram 3.4: Velocity reduction on the centre tunnel

57

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latest from the required triggering time onwards. The 64 km/h ODB test has an Firing Time Requirement (FTR) of 28 ms for the vehicle considered here. At this point, however, the velocity reduction of the RCAR test is still greater than that of the Fire load case. A separation is therefore not possible on the basis of this signal. Only from approx. 72 ms onwards, a threshold could be set. However, this would be significantly too late. The behaviour is mainly due to the type of barrier used. The ODB barrier is relatively soft at the beginning of the accident and is only collapsing further during the progress of the crash. This results in a rather flat vehicle velocity reduction curve. The RCAR crash is much slower, but due to the hard barrier the velocity of the vehicle is reduced comparatively quickly. Similar problems can be found when comparing other load cases with each other. Based solely on the overall vehicle deceleration, a stable algorithm is therefore no longer possible within the framework of today’s requirements. As has already been described, the algorithm is therefore supplemented by further signals that allow a clear differentiation of these critical load cases for the design. For example, another sensor is used, which is positioned in the vehicle in such a way that it provides clear signals for the ODB load case and not for the RCAR load case. Diagram 3.5 shows this. The signals for both load cases differ considerably, so that the algorithm can influence the triggering threshold shown in Diagram 3.6 on the basis of this information in order to enable a timely triggering of restraint systems. For example, the threshold shown in red is moved as soon as the additional sensor (as shown in Diagram 3.5) has detected that it is an ODB crash. Triggering in an RCAR crash is thus avoided and the timely triggering of the restraint systems for the ODB crash is ensured.

3.5 Airbag Algorithms

Diagram 3.5: Velocity reduction at an external sensor

Diagram 3.6: Influence of information from other sensors on thresholds

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New Algorithm Concept and Simulation Model

4 New Algorithm Concept This chapter explains the basics of the new algorithm concept. The principle of the local component-specific load is introduced and a comparison between an algorithm based on a overall vehicle deceleration and the new concept is carried out on the basis of measurement signals.

4.1 Motivation for a New Algorithm Approach Chapter 3 describes how current algorithms work on the basis of overall vehicle deceleration. It has been shown that the possibilities of influencing the result of the algorithm with a single degree of freedom, the threshold fitting, are rather small. In addition, it could be seen that the information content that can be determined from overall vehicle deceleration is limited. In the context of the expansion of the requirements in recent years, the potential to solve the requirements in a single-stage algorithm concept has proven to be exhausted. Current algorithms are therefore usually at least two-stage with a further measurand which must contribute to finding a solution in an upstream first algorithm stage. In addition, the roadmaps of legislators and consumer test organisations show a significant upward trend when it comes to new requirements in the coming years. For this reason, knowledge about the exact progress of a crash, its classification and information about hit direction is becoming increasingly important. Figure 4.1 shows an example of the Oblique load case and a load case with an Moving Progressive Deformable Barrier (MPDB). In addition, in oblique crash scenarios, it is difficult today to control front restraint systems differently on the side facing the impact than on the side facing away from the impact. A overall vehicle deceleration can only provide this information to a very limited extent. However, there is potential here for future requirements

© Springer Fachmedien Wiesbaden GmbH, part of Springer Nature 2020 A. Leschke, Algorithm Concept for Crash Detection in Passenger Cars, https://doi.org/10.1007/978-3-658-29392-5_4

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Figure 4.1: New requirements of consumer test organisations and legislators

to reduce occupant stress levels through the targeted triggering of restraint systems. In the context of this work, a completely different measurement method is presented to solve these problems. This measurement method forms the basis for a new algorithm approach. In addition to vehicle deceleration, the chronological sequence of an accident and the associated spatially and temporally resolved destruction of the vehicle, as equivalent to the energy introduced in the accident, are possible indicators for determining the severity of an accident [Kramer, 2013]. Figure 4.2 shows the destruction of the front end for a crash type with increasing crash speed. An increase in the destruction of the front end with increasing crash speed can clearly be seen. Figure 4.3 shows that, at the same speed but with different crash constellations, a significantly different destruction pattern occurs. The aim of this work is to investigate whether a new algorithm can be derived from this behaviour that offers advantages over the approach based on overall vehicle deceleration and answers the research questions posed in Section 1.4.

4.1 Motivation for a New Algorithm Approach

Figure 4.2: Destruction behaviour in the same crash load case at increasing speeds

Figure 4.3: Destruction behaviour in different load cases at the same speed

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Figure 4.4: 64 km/h ODB color-coded load wave Δv in [m/s]

4.2 Local Component-Specific Loads As already mentioned, the front end deforms in a characteristic and reproducible manner during a crash. In order to be able to evaluate this behaviour in an algorithm, a suitable measurement concept must first be developed. A concept for a new crash sequence evaluation variable must then be derived from this measurement concept. During the progress of the accident, the components and body structures located in the area of the front end are subjected to varying degrees of stress. A simulative evaluation of the 64 km/h ODB crash illustrated in Figure 4.2 shows that the destruction of the front end can also be represented in a temporal and local sequence of the loads of individual components located there. This behaviour can be represented graphically by a load wave as shown in Figure 4.4. The loads on the components, starting at the point where the opponent of the accident meets the ego vehicle, spread wave-like in the direction of the impact. The load pattern corresponds very well with the destruction of the front end. For visualization, the deceleration of the local components has been converted by integration into a local velocity reduction. The exceeding of a load limit has been color-coded in steps of 2 m/s from grey, i.e. no local velocity reduction (at this time still no load / deformation of the components) to brown, i.e. a velocity

4.2 Local Component-Specific Loads

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Figure 4.5: 32 km/h Wall 0° color-coded load wave Δv in [m/s]

reduction threshold of 10 m/s is exceeded. The crash load case illustrated in Figure 4.4 shows, as can be expected in an impact with 40% overlap, an asymmetrical behaviour with load progress from the left-hand side front diagonally through the front end. After approximately 50 ms, the high loads > 10 m/s have reached the bulkhead of the vehicle. If another load case with a symmetrical point of impact, e.g. the 32 km/h Wall 0° load case in Figure 4.5, is considered, it becomes apparent that the load wave propagates very differently. In this case the wave also starts at the point of impact and propagates towards the bulkhead. However, here the symmetrical point of impact and the straight propagation in vehicle x-direction can be clearly distinguished from the oblique wave in Figure 4.4. Although the Wall 0° load case is executed at a speed that is only about half as high, it becomes clear that the load at the same point in time within the crash is higher than in the 64 km/h ODB crash. The influence of the hard barrier in the Wall 0° crash in relation to that of the deformable barrier in the ODB crash can be seen. Despite the destruction being less severe, the components are more quickly exposed to a higher load in the form of a faster velocity reduction. The two waves can be clearly distinguished between, both in their propagation form and in their temporal sequence. If further crash load cases are considered, it can be seen that their load waves also differ considerably across the different crash

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types and speeds. This behaviour will be investigated systematically hereinafter, and a suitable algorithm concept will be derived.

4.3 Dynamic Behaviour of Cars in Various Accident Constellations The dynamic behaviour of passenger cars in the various frontal accident scenarios is essentially influenced by the structure of the body and by the arrangement of the components in the front end. Thus, the design of the load-bearing structures essentially determines the behaviour described in Chapter 3 in the interaction of deformation zone and rigid passenger compartment [Bubb and et. al., 2016].

4.3.1 Structure of the Front End, Body Structure and Components In state-of-the-art algorithms, the deceleration of the entire vehicle in a crash is measured (Complete Vehicle Algorithm, CVA). In addition to the weight of the vehicle, the elements of the body structure through which the energy of the other party to the accident is introduced into the vehicle play a significant role in the behaviour of the vehicle as a whole. Essentially these are the cross and/or side beams with their upstream crash boxes and the downstream load paths consisting of centre tunnel, sills and roof frame [Anselm and Danner, 1985]. Figure 4.6 shows a vehicle structure using the example of a medium-sized vehicle. The design of this structure ensures that the collision energy generated in an accident can be introduced into the body via defined load paths and can be dissipated there. Figure 4.7 shows the load paths resulting from the setup shown in Figure 4.6. In an algorithm that evaluates the load / destruction of the front end (Crash Intensity Algorithm (CIA)), the supporting structures play a subordinate role. Since the singular loads on the individual components in the front end are evaluated separately here, the arrangement and connection of these components to each other and to the structure accommodating them is of much greater importance here. Figure 4.8 shows the typical structure of a front end using a medium-sized vehicle as an example. With regard to details, the structure and

4.3 Dynamic Behaviour of Cars in Various Accident Constellations

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Figure 4.6: Body structure of medium-size vehicle [Volkswagen Aktiengesellschaft Academy Sales & Service, 2009]

Figure 4.7: Load paths of energy input into a vehicle during a frontal crash [Volkswagen Aktiengesellschaft Academy Sales & Service, 2009]

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Figure 4.8: Structure of a front end using the example of a medium-sized vehicle [Volkswagen Aktiengesellschaft Academy Sales & Service, 2013b,a]

distribution in the front end differ across vehicles and manufacturers. However, essentially the same components are used in a comparable installation space with similar body concepts, so that the procedure presented in the following can be transferred to other manufacturers and vehicle types. In principle, more and more vehicle manufacturers are switching to cross-vehicle class concepts that are also referred to as matrix platform systems. On new vehicle models in particular, the structure of the front end is therefore almost identical on all vehicles of one and the same vehicle manufacturer. [Achleitner and et. al., 2016] All these components are loaded at different time intervals during the crash and reduce their velocity differently depending on their own weight, connection to the environment, hit direction and hit intensity. In the following chapters, the behaviour described is illustrated using a vehicle from the small car segment. Figure 4.9 shows the front end and body structure of this vehicle. As can be seen, the structure is similar to that of the medium-sized vehicle shown in the previous illustrations. Basic elements, such as the side members, the cross member or the radiator with its frame and basic structure, can be found across all vehicle classes.

4.3.2 Requirements on an Algorithm In order to correctly assess the effects of an accident and to find a triggering algorithm for the controlling of restraint systems, it is essential to understand

4.3 Dynamic Behaviour of Cars in Various Accident Constellations

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Figure 4.9: Structure of the front end using a vehicle from the small car segment as an example

the data generated in a crash. The most important data used in conventional algorithms (CVA) are generated by the deceleration of the entire vehicle during the accident. In the new Crash Intensity Algorithm (CIA), however, local loads on components are to be used as input data for the algorithm. In the following chapter, the different signals, which form the basis of both approaches, are compared with each other and the local loads are examined with regard to their basic suitability for an airbag algorithm. The core task of the airbag algorithm is to detect the various crash load cases in good time. For this purpose, it is necessary to be able to establish a correlation between the measurement signals and the features used to differentiate between crashes, i.e. collision speed and crash type. This leads to two mandatory main requirements on an algorithm: 1. For one and the same crash type, increasing collision speed leads to increasing measurement signals. 2. Different crash types at the same speed lead to different measurement signals. In order to optimize the triggering strategy of restraint systems, further additional requirements arise, which should ideally be fulfilled:

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3. The crash direction (left or right) can be determined unambiguously. 4. The different crash types can be classified unambiguously. 5. Misuse or NoFire load cases should lead to a measurement signal that is as low as possible compared to the Fire load cases. For further considerations, the overall deceleration of the vehicle in the crash is measured at a point in the vehicle that is fixed to the body and that is not deformed in the crash. This measuring point is located on the centre tunnel between the two front seats. For the signals of the local componentspecific loads, signals from various measuring points in the front end are used.

4.3.3 Measurands and Coordinate Systems For the analysis of the data, it is necessary to know which coordinate system is used for the data. A distinction is made between a fixed coordinate system and a moving coordinate system. In a fixed coordinate system, the accident scene is "observed from the roadside". The measured velocity of the vehicle starts at the velocity above ground of the passenger car immediately before the start of the crash (v0 ) and is then reduced as follows: 

t1

v(t 1 ) = v0 +

a(t)d t

(4.1)

0

In the context of this work, the data analysis takes place in a moving data system. The accident is therefore viewed from a position within the vehicle. In a moving coordinate system, the measured vehicle velocity or the velocity of the components hit in the crash starts at zero and then proceeds according to the following formula: 

t1

v(t 1 ) =

a(t)d t

(4.2)

0

If the velocity function is based on the measurement of the overall vehicle deceleration, it also describes the velocity of an occupant not wearing a seat

4.3 Dynamic Behaviour of Cars in Various Accident Constellations

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Diagram 4.1: Overall vehicle deceleration and resulting velocity for a 64 km/h ODB load case up to 200 ms

belt relative to the passenger compartment, which in this coordinate system corresponds to the velocity change of the vehicle during the crash. The integral of the velocity function is as follows: 

t1

d(t 1 ) =

v(t)d t

(4.3)

0

The resulting function is an approximation of the forward displacement of an unbuckled occupant in the vehicle during a crash. The assumption is that a passenger who is not strapped in can be represented by a point mass. In reality, the movement of the occupant, even when not strapped in, is influenced by seat geometries and biomechanical behaviour. However, the values calculated in such a simplified way are used to determine the optimum triggering time in the crash application as described in Section 3.5 [Chan, 2000] In Diagram 4.1 and Diagram 4.2, the procedure is shown as an example for a measuring point on the centre tunnel as a reference for the total vehicle deceleration.

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Diagram 4.2: Velocity of the occupant and resulting forward displacement for a 64 km/h ODB load case up to 200 ms

Diagram 4.1 shows the overall vehicle deceleration measured on the centre tunnel for a 64 km/h ODB load case. If the signal is integrated, this results in the vehicle velocity reduction. According to the vehicle coordinate system used, the vehicle starts at 0 m/s and then reduces the speed to -18 m/s or -64 km/h. Diagram 4.2 shows the velocity reduction of the vehicle for the same load case. In a overall vehicle measurement, this is equivalent to the deceleration acting on an unbuckled occupant. Accordingly, this can be transferred to the forward displacement path of the occupant by further integration.

4.4 Comparison of Different Acceleration Signals During the crash, different zones in the vehicle are decelerated differently. As a result, different measuring points in the vehicle show differences in the time sequence of the deceleration and in the amplitude of the measuring signal. The differences can be clearly seen in Diagram 4.3. The sensor on the centre tunnel shows the deceleration in the centre of gravity of the vehicle. This part of the occupants’ rigid residual space is not deformed in almost all vehicle accidents. In this area between the front seats, sensors are usually installed which are used

4.4 Comparison of Different Acceleration Signals

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Diagram 4.3: Comparison of acceleration signals from centre tunnel sensor and front end sensor for a 64 km/h ODB load case up to 200 ms

to measure the deceleration of the entire vehicle in a crash. Since the sensors are neither hit directly in a crash nor influenced in their alignment in the vehicle’s coordinate system, the signals recorded there are directly proportional to the behaviour of the vehicle as a whole, provided that the passenger cell is perfectly rigid. This signal behaviour is evaluated by most known CV airbag algorithms for crash detection. In this example, the sensor in the front end is positioned on the assembly carrier. It shows the local loading of the component at this position in the front area of the front end and thus in the middle of the direct deformation area during the crash under consideration. In the crash at 64 km/h shown in the illustration, the unfiltered maximum acceleration signals of the sensor on the centre tunnel are 44 ms and −1,100 m/s2 . The first large signal peak with 50% of the maximum amplitude is at 38 ms. The signal behaviour of the sensor in the front end is much more pronounced and faster. Already after 14.1 ms the maximum signal peak is at a height of −5,010 m/s2 . The first signal peak greater than 50% of the amplitude is even reached after 8.2 ms.

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Diagram 4.4: Comparison of the deceleration signals of three sensors in the front end at a 64 km/h ODB load case up to 200 ms

Diagram 4.4 shows the deceleration signals from three different measuring points in the front end. It becomes very clear that the signals differ noticeably in their amplitude and in their temporal behaviour. All three are located on the same component, the plastic assembly carrier, approximately at the height of the radiator. They are distributed from the outside left to the middle to the outside right. In the ODB crash shown here with the hit point on the left, the propagation of the load wave in the front end can be followed very well using the sensors. The left sensor reaches its first significant peak significantly before the middle and right sensor. The maximum amplitude also decreases from left to right. This behaviour forms the basis of the CI algorithm.

4.4.1 Influence of Collision Speed on Deceleration Signals Diagram 4.5, Diagram 4.6 and Diagram 4.7 show the behaviour of the measurement signals when comparing the same crash type at different speeds. Two

4.4 Comparison of Different Acceleration Signals

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Diagram 4.5: Comparison of deceleration and velocity signals of the overall vehicle in the 27 km/h Wall 0° load case to the 56 km/h Wall 0° load case

Wall 0° load cases with 27 km/h and with 56 km/h are shown. The picture is similar for the overall vehicle signals and for those of the local components. Both the acceleration values and the resulting decelerations can be clearly distinguished from each other. The first main requirement on an algorithm from Section 4.3.2 is well fulfilled for both input variables. If the values for the overall vehicle are considered, it can be seen that the deceleration values are in the range of max. 1,600 m/s2 after 19 ms for the 56 km/h load case and 800 m/s2 after 23 ms. The speed is reduced to virtually zero after approximately 60 ms for a 27 km/h load case and after 80 ms for a 56 km/h load case. The measured values of the two measuring points of the local component-specific load behave very differently. Diagram 4.6 shows a measuring point directly on the outer skin of the bumper and Diagram 4.7 a measuring point on the side beam approximately 250 mm further inside the vehicle. The propagation of the load wave in the vehicle is clearly reflected by the measurement signals. At both measuring points, the measuring signals of both load cases can be clearly distinguished. If, for example, the measuring point directly on the outer skin of the vehicle is considered, it becomes apparent that the velocity

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Diagram 4.6: Comparison of deceleration and velocity signals at a measuring point on the outer skin of the bumper in the 27 km/h Wall 0° load case to the 56 km/h Wall 0° load case

Diagram 4.7: Comparison of deceleration and velocity signals at a measuring point on the side member in the 27 km/h Wall 0° load case to the 56 km/h Wall 0° load case

4.4 Comparison of Different Acceleration Signals

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there is reduced extremely quickly after 4 ms. Compared to the overall vehicle load, significantly greater decelerations occur. The maximum deceleration is 62,000 m/s2 and thus 50 times as high in comparison. The measuring point further inside the vehicle is also subjected to significantly higher loads. The largest signal peak of the deceleration is still 15,000 m/s2 , which is 10 times higher, and the velocity reduction of the 56 km/h load case is almost complete after 20 ms.

4.4.2 Influence of the Collision Type on the Deceleration Signals To compare the signal characteristics of different types of collisions, two load cases are analysed, which are driven at almost the same speed. The first is an oblique load case (32 km/h Wall 30° left), the second a 30 km/h ODB load case. As shown in Diagram 4.8 the destruction behaviour of both load cases can be distinguished clearly. In an oblique crash, such as the 30° Wall crash with its hard barrier structure, the vehicle is decelerated relatively quickly at the hard load-bearing structures such as the side member and is subject to a sudden large load. The ODB barrier is initially soft and builds up its deceleration torque over time. The deceleration curve of the vehicle is therefore rather uniform at a lower level. Both load cases can be easily distinguished between based on the deceleration and velocity reduction. However, it is noticeable that a separation via velocity reduction is only possible at a late stage (Diagram 4.9). A distinction is not possible until after 50 ms. This is clearly too late for the triggering of restraint systems in these load cases. At the two measuring points in the front end the result is much better. A clear advantage of using signals in the front end is obvious. A distinction between the two load cases is clearly possible and the separation of the velocity reduction curves takes place much earlier. The two load cases, shown in Diagram 4.10 and Diagram 4.11, can be distinguished after approximately 10 ms. In summary, it can be stated that both measurement methods fulfil the second main requirement on an algorithm. These examples show very clearly that the

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Diagram 4.8: Comparison of deceleration and velocity signals of the overall vehicle in a 32 km/h Wall 30° load case to a 30 km/h ODB load case

Diagram 4.9: Comparison of the velocity signals of the overall vehicle in a 32 km/h Wall 30° load case to a 30 km/h ODB load case

4.4 Comparison of Different Acceleration Signals

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Diagram 4.10: Comparison of the deceleration and velocity signals at a measuring point on the outer skin of the bumper in a 32 km/h Wall 30° load case to a 30 km/h ODB load case

Diagram 4.11: Comparison of deceleration and velocity signals at a measuring point on the side member in a 32 km/h Wall 30° load case to a 30 km/h ODB load case

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Diagram 4.12: Comparison of deceleration and velocity signals of the overall vehicle in 40 km/h Wall 30° load cases with hit directions on left and right

use of measuring points in the front end has a time advantage over measuring points on the centre tunnel.

4.4.3 Influence of the Collision Direction on the Deceleration Signals In Diagram 4.12, two load cases with the same crash type and speed but with different directions are compared. As in the previous chapters, a measuring point on the side member is used to evaluate the measuring points in the front end. Two 40 km/h Wall 30° load cases are compared - once with a hit position on the left and once with a hit position on the right. If the measuring points of the overall vehicle signal are considered, it becomes apparent that they only differ marginally for both load cases. A distinction between left and right is not possible. The third secondary requirement on an algorithm cannot be fulfilled. When comparing other load cases with each other, the result is the same. The behaviour at the measuring points in the front end as shown in Diagram 4.13 is completely different. At a measuring point on the left side beam, it is very

4.5 Duration of the Crash, Algorithm Runtime and Start Time

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Diagram 4.13: Comparison of deceleration and velocity signals at a measuring point on the side beam in 40 km/h Wall 30° load cases with hit directions on left and right

easy to see that both hit directions lead to different signal characteristics. The distinction between left and right can easily be made. The same behaviour can be seen with other load cases and other measuring points in the front end. The third secondary requirement on an algorithm can easily be fulfilled using the input signals of the CI algorithm. This example clearly shows that the CI algorithm has advantages over the CV algorithm.

4.5 Duration of the Crash, Algorithm Runtime and Start Time The runtime is an essential factor in algorithm design. The runtime is the time from the start of the evaluation of the measurandsuntil the termination of the evaluation. The start of the runtime is usually the beginning of the accident, i.e. the time a measurand exceeds a defined value. Similarly, the termination is defined as the time the defined threshold is undershot. Runtimes that are too long lead to problems. One such problem is that the signals of several events, e.g. multi-collisions, mix with each other. Another problem is that an integration

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Diagram 4.14: Comparison of the velocity signals of the complete vehicle for different load case types

of measurand over too long a period of time can result in the velocity signal building up due to the oscillation processes that occur permanently during driving. To determine the runtime, it is therefore necessary to analyse the progress of various Fire and Misuse load cases. To determine the algorithm runtime, the time of the Fire load case with the longest runtime should be selected. The start time of the algorithm results from a condition that is to be defined. In this paper, the t0 point, i.e. the algorithm start for the overall-vehicle algorithm, is defined as the point in time at which the measurand exceeds a value of 10 m/s2 for the first time. With this procedure a clear demarcation to the loads that occur during normal driving operation is ensured, and a permanent algorithm restart is avoided. In typical frontal accidents, a significant velocity reduction can be observed in an interval of up to approximately 150 ms (Diagram 4.14). Since the lower limit of a required restraint system design is taken into account via this load case set and since it can also be observed that an increase in the crash speed always leads to a shortening of the crash duration, the runtime of the crash algorithm can be limited to 150 ms. In addition, in the diagram, the two categories of load cases with fast and slow crash severity progress that have already been

4.6 State-of-the-Art Algorithm Compared with the Requirements

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Figure 4.10: State-of-the-art algorithms compared to stated requirements

described can be seen. The runtimes of the fast crashes are shown in red with 60 ms to 80 ms and the runtimes of the slow crashes are shown in blue with 100 ms to 150 ms. The crash duration is characteristic both for the type of crash and for the speed of the crash.

4.6 State-of-the-Art Algorithm Compared with the Formulated Requirements In Section 4.3.2, requirements on an algorithm were formulated which should be fulfilled as comprehensively as possible by the new CI algorithm proposed in the following chapters. As a starting point for a later comparison, the requirements are compared with the current state-of-the-art according to [Kozyref, 2004]. Figure 4.10 shows the result. The first requirement on a correlation between the measurand used and the increasing crash speed is fulfilled in the current state-of-the-art with a thresholdbased CV algorithm. The second requirement is essentially met, but shows weaknesses with regard to new load cases. These weaknesses need to be compensated by the use of more and more additional information. The fulfilment of

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the three additional requirements is subject to strong restrictions. In particular, a differentiation from NoFire load cases is only possible by using additional sensors. Furthermore, a large number of Misuse load cases provide high overall vehicle deceleration signals, especially in the first milliseconds after t0 , that make separation from the Fire load cases difficult. The third and fourth requirement are not fulfilled. The missing input information in the left / right differentiation, as an example of a classification, suggest that the classification of further load case types is also not possible due to the missing variance in the input variable.

5 Model for the Description of Threshold-Based Algorithms The following chapter proposes a methodology according to which airbag algorithms can be grouped in a three-dimensional model according to their input and output variables, their processing stages and their temporal behaviour.

5.1 Structure of Algorithms Basically, almost all known threshold-based algorithms follow the structure shown in Figure 5.1 [Beucher, 2018]. If this generally accepted approach is applied to the sequence of an airbag algorithm, the following new model is obtained. It is divided into four phases: Information retrieval For the algorithm, measurands must be determined which, due to their information content, provide information relevant to the crash algorithm. As a rule, the measurement signal represents a measurand that is directly proportional to the crash under consideration. The measurand can be used either for a part or for the entire load case spectrum under consideration. The information can be determined by one or more sensors. They can provide the same or different signal information. Typical examples are acceleration sensors with deceleration signals as measurands. These are installed at one or more points in the vehicle. Signal pre-processing In the signal pre-processing block, the various measurement signals are preprocessed according to the following algorithm, and decision values are generated. In the state-of-the-art algorithms shown in the previous chapter, these

© Springer Fachmedien Wiesbaden GmbH, part of Springer Nature 2020 A. Leschke, Algorithm Concept for Crash Detection in Passenger Cars, https://doi.org/10.1007/978-3-658-29392-5_5

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Figure 5.1: Structure of threshold-based algorithms based on [Beucher, 2018]

are, for example, the calculations of the velocity reduction in the overall vehicle. Algorithm In the algorithm block, the decision values are evaluated with regard to one or more thresholds. If the threshold is exceeded, this information is passed on to the corresponding action concepts or, in the case of multi-stage algorithms, the influence of further thresholds is initiated. Action concept Depending on the threshold crossing in the algorithm block, the action concept decides how to proceed with this information. As a rule, the corresponding restraint systems are triggered here or classification information is evaluated for further processing.

5.2 Classification Model of the Algorithm Block The algorithm block can be further divided into classes based on its structure. A division into three levels has proven to be effective.

5.2 Classification Model of the Algorithm Block

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Figure 5.2: Instance model

The first classification is based on the question of whether the algorithm passes only one or more independent pieces of information on to the subsequent action blocks. Figure 5.2 shows this instance model. In accordance with the algorithm structure, an algorithm is referred to as singleinstance algorithm if one or more input signals for a single algorithm threshold lead to a single action concept. In addition to this algorithm, which is based on a single instance, algorithms with several parallel instances are possible. In a two-instance approach, two algorithm thresholds that are independent of each other in terms of content are on a par with each other. Based on these two algorithm thresholds, two different action concepts are executed. The model can be extended to n instances as required. The second classification shown in Figure 5.3 is based on the question whether the action concept is triggered in a single-step processing step or whether several algorithm thresholds have to be passed one after the other before a decision can be made in favour of an action concept. In a one-step model, exceeding an algorithm threshold triggers an action concept. In a two-step approach, exceeding one algorithm threshold leads to a second algorithm threshold being influenced. An action concept is triggered on the basis of this second level being exceeded. The model can be expanded analogously to n levels. The third classification shown in Figure 5.4 is based on the question whether different load cases on the basis of the same decision value in an algorithm

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Chapter 5 Model for the Description of Threshold-Based Algorithms

Figure 5.3: Graduated model

Figure 5.4: Time model

block provide a clear result over the entire time horizon or whether an evaluation of the threshold exceedance is only possible within certain time limits. An algorithm is time-independent if load cases can be evaluated and a clear decision can be taken over the entire time horizon under consideration solely based on the exceedance of a threshold. The load cases that should lead to the associated action concept safely exceed the threshold, all other load cases never do so. An algorithm is referred to as time-dependent if the exceedance of the threshold for the load cases in question only leads to the desired result within a limited time window. Outside this time window, other load cases may

5.3 Evaluation of the State-of-the-Art in the Classification Model

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Figure 5.5: Evaluation of the algorithm concept according to the state-of-the-art in the algorithm model

exceed the threshold. If temporally dependent components are used within an algorithm concept, it must always be ensured that unintentional decisions in the algorithm that are caused by this temporal restriction are prevented via other mechanisms. Based on the overall model presented here, all algorithms described in Chapter 3 can be classified. Basically, it becomes clear that algorithms are advantageous that have the simplest possible structure and provide the most diverse information possible. Algorithms that have as many instances as possible with as few steps as possible are advantageous. In addition, time-independent algorithms are always to be preferred.

5.3 Evaluation of the State-of-the-Art in the Classification Model In Chapter 3, an algorithm concept according to [Kozyref, 2004] was presented which represents the state-of-the-art in almost all current vehicles. This characteristic of the CV algorithm is evaluated in the following according to the proposed classification model and is used in Part III of this work as an essential state-of-the-art, also for the comparison with the results of the CI algorithm. The result is shown in Figure 5.5. The current airbag algorithm only has one firing decision available as output variable. Therefore, it is referred to as a model with only one instance. The main input variable in the algorithm is not sufficient to make a clear decision at the algorithm output. The decision threshold must be influenced by

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another independent input variable in order to perform a threshold adjustment, i.e. this is a two-stage concept. The evaluation of the measured variable is not limited in time in the algorithm. It is not relevant at which time the measured variable exceeds the trigger threshold. Therefore, this algorithm is time-independent.

6 Simulation Model for Component-Specific Local Load In this chapter, a methodology is proposed how a simulation can be set up on the basis of finite elements in order to evaluate the load on the components in the front end during an accident. For this purpose, the front end is geometrically divided, and the simulation model of the vehicle is extended to include sensor measuring points in line with the resulting grid. It is shown how a simple evaluation variable can be generated from the simulation values of the component decelerations. This variable can then be used as an input for the CI algorithm. Generation of the evaluation variable is via a new tool-supported procedure.

6.1 Structure of the Simulation Model The systematic investigation of the deformation of the front end over time is based on simulation methods such as those used to evaluate questions in the crash environment within the industry [ESI Group, 2011]. A calculation tool called PamCrash produced by ESI is used. The simulation tool is based on the finite element method. First, the structure of the vehicle is broken down into geometrically simple elements and defined via calculation nodes. Subsequently, a displacement approach is specified, which mathematically represents the element deformation behaviour from the unknown node displacements. The equilibrium conditions at the element nodes lead to a system of equations from the solution of which the node displacements can be determined. From this, the accelerations and thus the signal characteristics of the calculation nodes can be determined with the help of the temporal node displacements. [Kramer, 2013] The basis of this work is a complete vehicle model that corresponds to the structure of a typical small car model as described in Section 4.3.1 [Siela and Recke, 1990]. For the systematic investigation of the deformation over time,

© Springer Fachmedien Wiesbaden GmbH, part of Springer Nature 2020 A. Leschke, Algorithm Concept for Crash Detection in Passenger Cars, https://doi.org/10.1007/978-3-658-29392-5_6

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Figure 6.1: Systematic distribution of the measuring points in the front end of the vehicle

the front end of the vehicle is geometrically divided into zones in the vehicle coordinate system in the x-, y- and z- axes. Measuring points to measure the propagation of load waves are arranged in these zones. Arrangement preferably is in such a way that it is symmetrical. Based on the structure of the front end concept used in this work, a division into 11 planes on the x-axis (approximately every 100 mm), 13 planes on the y-axis and 3 planes on the z-axis proves to be advantageous (Figure 6.1). To determine the local load values, the calculation nodes that correspond to the measuring points are provided with uniaxial virtual acceleration sensors that are

6.1 Structure of the Simulation Model

95

oriented in the x-direction. The orientation is based on the orientation of the components in their installation situation before the crash. The following investigations are based on a specific vehicle model from the small car segment. However, the considerations on which they are based are applicable to all vehicles as long as the bodywork in the area of the front end is not fundamentally different. As shown in Section 4.3.1, the vehicle concepts of one and the same manufacturer are found to be increasingly similar with regard to the front end structure, even across vehicle classes. This is due to the manufacturers using a platform strategy. The basic position of the sensors can therefore be kept constant across several vehicle segments. A transfer to other vehicle manufacturers or vehicle concepts is basically possible while maintaining the same methodology. Only the structure of the geometric sensor distribution must be adapted accordingly. The basic procedure, however, always remains the same. If the above system described in Figure 6.1 is applied to the model used for the following investigations, it becomes apparent that the real distribution of the components in the vehicle does not always allow this purely geometric arrangement to be followed, but that it is in principle possible to remain in a continuous, almost symmetrical grid. In order to enable simple evaluation of the measuring points, the names of the measuring points are structured according to a nomenclature which makes it possible to assign them easily to certain geometric zones in the vehicle. The names used for the measuring points are as follows: S_E_CO_H_K With: • Sensor name (S) = GSAT • Plane (E) = E1 to E11 • Component (CO) = components on which the sensors are located QT KPV/H DFE LT

cross member radiator package front / rear deformation element side member

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Chapter 6 Simulation Model for Component-Specific Local Load

MB/V/H BT SWF RI SW KFA FBD F

engine block, side / front / rear battery headlights wheel arch inside bulkhead fender suspension strut dome front area / bumper

• Height (H) T M B

top mid bottom

• Coordinate (K) = direction and distance from centre of vehicle in y-direction m 0 p

minus vehicle centre plus

e.g. GSAT_E1_F_M_p0400 This means that the sensor shown in Figure 6.2 is called GSAT, is located in the first plane, is attached to the front end, in the middle plane in z and on the position + 400 in y-direction. In Figure 6.3 the other individual planes are shown in detail in the x-section. In total, 219 measuring points were integrated into the PamCrash model in the described systematics and are thus available for load simulation. Chapter 3 presented a set of typical Fire, NoFire and Misuse load cases. All these load cases with their simulation data form the basis for the following investigations. Twelve Fire load cases were considered (as shown in Table 3.2), five of which were considered with a right and a left impact point due to their oblique portion or asymmetrical structure. In addition, there are seven typical NoFire requirements and nine Misuse scenarios from Table 3.1. Compared to a CV algorithm, the number of Misuse load cases to be considered is significantly smaller, since all vehicle decelerations that are only introduced via the chassis are negligible

6.1 Structure of the Simulation Model

97

Figure 6.2: Plane 1

by definition because they do not cause any significant destruction of the front end. Therefore only accidents with wildlife are considered which lead to the greatest destruction of the vehicle structure in comparison to the other misuse accidents. In total, this results in a set of 33 load cases that were simulated. For each of these load cases there are 219 local deceleration curves. A set of 7,227 individual sensor deceleration curves is therefore available for evaluation. The signal characteristics were uniformly calculated up to a duration of t1 = 300 ms to determine the long-term progress of the crash. Typical frontal crash load cases have generally reduced their velocity to 0 m/s after a maximum of 150 ms and require triggering times for restraint systems of a maximum of 50 ms, but some NoFire events show a significantly longer temporal progress. After simulation, the values are sampled at 20,000 Hz and stored for further processing. In accordance with the runtime of 300 ms, 43,362,000,000 measured values are available. Diagram 6.1 shows the curves of individual selected measuring points for a Wall 0°, a Wall 30° and a NoFire load case in their simulation environment.

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Figure 6.3: Plane 2 to Plane 11

6.2 Signal Pre-Processing

99

Diagram 6.1: Three load cases at four selected sensors

6.2 Signal Pre-Processing As already described, an algorithm based on pure deceleration signals does not make sense. The height and temporal position of the individual peaks vary greatly within the framework of test scattering and tolerances in the production of the individual vehicles. Therefore, the CI algorithm is to be built up on the basis of processed signals. The deceleration signals generated during the simulation are converted into a velocity reduction according to the signal processing already shown in Section 3.4.3. The signals are available after pre-processing in a 2,000 Hz raster. The pre-processing process is shown using the example of a 56 km/h Wall 0° load case at the sensor measuring point GSAT_E2_QT_M_00000. The raw signal described in Diagram 6.2 has amplitudes between −27,000 m/s2 and +12,000 m/s2 . The signal runs out at approximately 30 ms. After passing through the filter chain, the amplitudes in the smoothed signal in Diagram 6.3 are reduced to −12,000 m/s2 and +2,500 m/s2 . The signal continues to run out at approximately 30 ms. Through integration, the velocity reduction of the local component to which the sensor is attached is determined from the filtered signal. Due to the pre-

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Diagram 6.2: Example of an unfiltered local deceleration signal

Diagram 6.3: Example of a filtered local deceleration signal

6.2 Signal Pre-Processing

101

Diagram 6.4: Example of a local velocity reduction signal

processing model used, the magnitude of the velocity reduction is used in Diagram 6.4. The velocity reduction at this sensor measuring point, which is mounted in the vehicle very close to the front, takes place very quickly. The 16 m/s, corresponding to the crash velocity of 56 km/h in this load case, are reduced to zero after approximately 6 ms. After a short rebound phase of 2 ms, the signal oscillates to the final value for this sensor. If this pre-processing process is carried out for all load cases and all sensor positions, a set of 219 velocity reduction curves is generated for the 33 load cases considered. Diagram 6.5 shows a selection of these for one load case. In order to keep the requirements on real-time data processing by the control unit in the vehicle as low as possible, the evaluation variable calculated from the measurement signals is simplified further. The calculated deceleration values of the individual sensors are converted into binary information for this purpose. As already shown in Figure 4.4, the load wave can be described by the exceedance of defined thresholds. This type of evaluation can be further simplified by evaluating only the exceedance of one defined signal threshold of the deceleration signals. The first time a local velocity reduction is exceeded on the component to which the sensor is attached is thus evaluated. Each

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Diagram 6.5: Selection of local velocity reduction curves for a 56 km/h Wall 0° load case

sensor only provides a purely binary information as input for the algorithm. A zero for the information that the threshold was not crossed and a one for the information that the threshold was crossed. A further goal for keeping the signal processing requirements as simple as possible is to use a single identical threshold for all sensors used in the respective vehicle. Matlab is used for further processing of the simulation data. Matlab is a software from the US company MathWorks. Its purpose is to solve mathematical problems and to graphically display the results [Angermann, 2014]. Matlab is primarily designed for numerical calculations using matrices and offers the possibility to create complex models for data preparation [Schweizer, 2013; Benker, 2000, 2010]. Diagram 6.7 shows the tool chain used in this work. The procedure is shown in Diagram 6.6 . This generates a staircase function that records the highest values over time. The pre-processed signal curves are evaluated in another calculation script created in Matlab. The script implements the sensor concept of threshold monitoring and can support technical boundary conditions of sensors and thus the evaluation of different sensor models. The exact procedure is explained in detail in Chapter 7 using an example. Various input parameters can be defined as input variables for the tool chain:

6.2 Signal Pre-Processing

Diagram 6.6: Holdmax post-processing step

103

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Chapter 6 Simulation Model for Component-Specific Local Load

Diagram 6.7: Matlab tool chain

• Holdmax threshold setting: This parameter defines the height of the threshold at which the measuring point switches from the "unloaded" to the "loaded" state. The sensitivity of the algorithm can be determined by varying this parameter. • Sampling time interval: For the present work, the sampling time interval of the thresholds is set to 2 kHz. Depending on the sensors used, this value can be adjusted. At 2 kHz, the range used in the state-of-the-art for vehicle acceleration sensors is used. • Selection of measuring points: This parameter can be used to select the measurement points / sensors to be included in the evaluation. The sensor set shown in the previous chapter is used for the basic investigations. The following artefacts are available as output variables of the tool chain for further processing: Holdmax function time table (Table 6.1) The actual calculation of the data for the previously defined threshold values takes place in the area of the tool chain. For this purpose, the current measured value is queried for each selected sensor position at each selected sampling point, the Holdmax threshold crossing is checked and written to a binary matrix. A time table is created for each selected load case. All measuring points with their temporal threshold exceedance curves for the respectively selected threshold

6.2 Signal Pre-Processing

105

Table 6.1: Holdmax function time table Sensor, Time in [ms]

0

0.5

1

1.5

2

2.5

3

...

295.5

300

GSAT_E1_F_B_00000 GSAT_E1_F_B_m0100 GSAT_E1_F_B_m0200 GSAT_E1_F_B_m0300 GSAT_E1_F_B_m0400 GSAT_E1_F_B_m0500 GSAT_E1_F_B_p0100 GSAT_E1_F_B_p0200 GSAT_E1_F_B_p0300 GSAT_E1_F_B_p0400 GSAT_E1_F_B_p0500 GSAT_E1_F_M_00000 GSAT_E1_F_M_m0100 ... GSAT_E11_SW_T_p0200 GSAT_E11_SW_T_p0400

0 0 0 0 0 0 0 0 0 0 0 0 0 .. 0 0

0 0 0 0 0 0 0 0 0 0 0 0 0 ... 0 0

1 0 0 0 1 0 0 0 0 0 0 0 0 ... 0 0

1 0 0 0 1 0 0 0 0 1 0 1 1 ... 0 0

1 0 1 0 1 1 0 0 0 1 0 1 1 ... 0 0

1 0 1 0 1 1 0 0 0 1 0 1 1 ... 0 0

1 0 1 0 1 1 1 0 0 1 0 1 1 ... 0 0

1 0 1 0 1 1 1 0 0 1 1 1 1 ... 0 0

1 0 1 0 1 1 1 0 0 1 1 1 1 ... 0 0

1 0 1 0 1 1 1 0 0 1 1 1 1 ... 0 0

are mapped in it. As described, the Holdmax function ensures that the one-time threshold crossing is recorded and leads to a further information Signal High (1) in all subsequent sampling steps. If the threshold is not exceeded, the information Signal Low (0) is stored for the respective time step. Totals table (Table 6.2) The totals tables are based on the time tables of all load cases and aggregate the histories stored there for the time period under consideration. On the basis of the information presented here, a comparison between the individual load cases is made and the possibility and quality of discrimination between the load cases is determined. True / False animation (Figure 6.4) For visual support of the data analysis, the data are converted into two animations, which are also generated automatically from the Matlab scripts. Figure 6.4 shows an example of a true / false animation. The temporal course of the simulation is prepared separately for each load case and each threshold considered. Measuring points that have exceeded the respective threshold are displayed in red. Measuring points below the threshold are displayed in green. Each individual time step of the simulation is available as a separate image. At the end of the simulation they are combined to form an animation for the entire course of time.

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Load case

GSAT_E1_F_B_00000

GSAT_E1_F_B_m0100

GSAT_E1_F_B_m0200

GSAT_E1_F_B_m0300

GSAT_E1_F_B_m0400

GSAT_E1_F_B_m0500

...

GSAT_E11_SW_T_p0200

GSAT_E11_SW_T_p0400

Table 6.2: Totals table

10 km/h RCAR Bumper left 10 km/h RCAR Bumper center 10 km/h RCAR Bumper right 35 km/h Pole ... 64 km/h ODB right

0 0 0 593 ... 593

0 0 0 589 ... 587

0 0 0 527 ... 583

0 0 0 456 ... 483

0 0 0 455 ... 473

0 0 0 451 ... 435

... ... ... ... ... ...

0 0 0 455 ... 480

0 0 0 451 ... 481

Figure 6.4: True / False animation and heatmap

Heat map (Figure 6.4) The Heatmap animation is an addition of all true / false animations and thus shows the propagation of the load caused by the vehicle crash across all considered boundary thresholds of the simulation.

Part III

Methods and Results

7 Algorithm for Local Component-Specific Load This chapter deals with the basics of the CI algorithm and introduces the new evaluation variables CI, CIT, CITn and MCITn.

7.1 Fundamentals of the Crash Intensity Algorithm In Chapter 3, requirements that result from laws, consumer tests and field events were presented. They form the requirements framework that must be fulfilled by a crash algorithm. As a minimum requirement, a newly developed algorithm must ensure timely triggering of the restraining devices in the load cases mentioned. In addition, Section 4.3.2 stipulates further requirements that must be fulfilled. If the requirements on a CI algorithm are described using the classification concept proposed in Section 5.2, then this results in the division shown in Figure 7.1. A seven-instance model is used to meet the requirements. The first instance shows the main algorithm. The timely release of the restraint systems and the insensitivity to Misuse events are implemented here. The classification algorithms are described in instances two to seven. Here, the individual load case groups are classified and the direction information is evaluated. All algorithms should be implemented via one decision level and the thresholds used should have an unlimited validity. As is the case with current CV algorithms, it must be possible to design the CI algorithm using only a few grid points. It therefore remains important that there is a constant correlation between the increasing crash severity and resulting measured variables and the decreasing release times of the restraint system. In principle, the threshold-based triggering concept shown in the previous chapters has proved its worth. The trigger performance of an algorithm that mathematically links physically determined signals remains physically explainable. Especially in an algorithm that can only access a very small number © Springer Fachmedien Wiesbaden GmbH, part of Springer Nature 2020 A. Leschke, Algorithm Concept for Crash Detection in Passenger Cars, https://doi.org/10.1007/978-3-658-29392-5_7

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Figure 7.1: Concept of the Crash Intensity algorithm

7.1 Fundamentals of the Crash Intensity Algorithm

111

of design load cases, this is essential to ensure that real accidents between the grid points result in timely triggering and that there is no discontinuity in the algorithm. In contrast to an algorithm that is only based on one signal such as the overall vehicle deceleration, the CI algorithm requires the development of a calculation rule that exploits the advantages of the additional information without losing the physical relationships. An essential question that must be answered is whether the information content of the sensor measuring points as presented in Chapter 6 is sufficient to meet the formulated requirements. As described in Chapter 3, the application of an algorithm that is based on total vehicle deceleration essentially consists of determining a suitable threshold on the basis of which the load cases result in a firing decision that meets the requirements. The position of the main sensor is determined by its location in the non-deformed part of the vehicle at a symmetrical position in y-direction. The algorithm does not offer any other control variables. Thus, the design of the trigger threshold is the only degree of freedom that can be influenced. The CI algorithm derives some of its advantages from the fact that it grants two additional degrees of freedom with the help of which the requirements can be fulfilled: • Selection of Holdmax threshold The parameter defines the threshold level at which the measuring point switches from "unloaded" to "loaded". The sensitivity of the algorithm can be influenced by varying this parameter. • Position and number of sensors in the front end The selection of sensors can influence the relationship between sensor signals and firing time requirements. If the total number of sensors provides total values for individual load cases that are too high compared to the firing time requirement that is necessary for these load cases, then specific sensors can be removed from the sensor set. If the total signals are too low, sensors can be added accordingly. This makes it possible to compensate for any imbalance between individual load cases. In the case of classification algorithms in particular, this opens up the possibility of specifically including those sensors

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in the decision that only make a significant contribution to the load case group or direction under consideration. • Design of the algorithm thresholds By determining the level of the respective algorithm threshold, the firing time for restraint systems or the classification times (CT) can be influenced. The shape of the thresholds allows further influence on firing times and misuse sensitivity. In the following chapters, a methodology is proposed for all three degrees of freedom on how to achieve a result that meets the requirements. The methodology is applied to the specific vehicle project considered here, and a comparison is made with the performance of the state-of-the-art CV algorithms.

7.2 Crash Intensity as a New Input Variable for the CI Algorithm The pre-processing shown in Chapter 6 forms the basis for the decision variables that are made available to the algorithm. As has been shown, two physical quantities are essential for all airbag algorithms: On the one hand, the load on the vehicle is to be evaluated. In a CV algorithm, this is done using the integral of the total vehicle deceleration. In the CI algorithm, this input information is obtained by evaluating the Holdmax function for each sensor. On the other hand, the time sequences during a crash are of importance. In Section 4.4, it could be seen that not only the level of the vehicle load, represented there by the deceleration signals, contributes to the differentiation of load cases and crash speed, but also the temporal relationships. In order to obtain the simplest possible algorithm and sensor construction as formulated in the requirements, the threshold crossing in the individual sensor must be enriched with the time information for further use as an input signal for the algorithm. The use of a mere time stamp for threshold exceedance has not proved to be effective. On the one hand, this increases the complexity and size of the output signal at the sensor, since it would now have to transmit a combination of Holdmax threshold exceedance and assigned time stamp. On the other hand, the use of a time in milliseconds leads to jumps in the signals, which are

7.2 Crash Intensity as a New Input Variable for the CI Algorithm

113

opposed to a continuous behaviour of the algorithm signal. When using time (e.g. threshold exceedance after 6 ms) as the basis for consideration, a measurement threshold that has not been exceeded would have to be assigned either the time unit 0 ms or ∞ ms. In a methodology designed for the comparison of many sensors, however, this leads to massive distortions of the evaluation variables. The use of zero as a variable distorts the evaluation to such an extent that sensors that trigger very quickly (e.g. at 1 ms) move into the immediate vicinity of 0 ms non-triggers. If infinity is used as the data size, evaluations change abruptly and unpredictably over time. If, for example, a measuring point that triggers at 11 ms is evaluated using a 10 ms time span, then it is considered to be infinite. When using a 20 ms time span, the same measuring point would be included with its real time of 11 ms. Both are not useful for data analysis. For this reason, it has proved to be more appropriate to highlight the distinguishing features using a decision variable that has been newly developed for this work: the Crash Intensity. This crash intensity, determined as the input variable of the algorithm, starts with a value of zero at each measuring point at the beginning of the simulation or at the beginning of the real signal recording at the sensor. As long as the Holdmax threshold value is not reached, there are still zeros for the scanning steps and the crash intensity does not increase. If the limit threshold value is exceeded, the Holdmax function keeps the value at 1 for this and all subsequent steps until the total crash duration is reached. The crash intensity increases by one increment for each considered time step in which the limit threshold was exceeded. In this kind of consideration, it is irrelevant whether a measuring point has never exceeded the limit threshold or only not in the time interval considered. No matter which time step is considered, there is always an additive increase in crash intensity per measuring point. The methodology is shown in Diagram 7.1 and Diagram 7.2. The left part of Diagram 7.1 shows the velocity reduction for a sensor. Between 1 ms and 1.7 ms, the speed reduction decreases again. The Holdmax function keeps the signal at the lowest peak reached during signal progress. For example, the signal does not rise again at 1.5 ms, but is kept at the same level up to 3 ms until it encounters a falling edge again.

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Diagram 7.1: Holdmax conversion

The crash intensity is determined from the pre-processed signal using the following calculation rule. For this purpose, the signal is sampled in the time pattern of 0.5 ms used for this work. Crash Intensity per sensor CIs :

C Is (LF )h =

T1 

(H ol d ma x h (Sensorsi g nal(LF )s ))(T )

(7.1)

T =0

With: LF h s Holdmaxh Sensorsignal(LF)s T T1 t1

load case under consideration level of the Holdmax threshold sensor under consideration result of threshold exceedance, 0 if undershot, 1 if exceeded signal characteristic of the sensor s in the load case LF sampling step number of sampling steps in the period under consideration t1 sampling time under consideration

Diagram 7.2 shows the determination of the CI using the velocity reduction from Diagram 7.1.

7.2 Crash Intensity as a New Input Variable for the CI Algorithm

115

Diagram 7.2: CI determination of a sensor

The Holdmax curve shown in Diagram 7.1 is sampled in 0.5 ms increments. The Holdmax thresholds can be set at any level. In this example, thresholds are shown in intervals of 2 m/s. The illustration shows the 6 m/s and 10 m/s thresholds. The Holdmax curve is sampled every 0.5 ms according to the calculation specification, and the crash intensity is determined from the sampled values. Both quantities are shown in Table 7.1 for both limit thresholds. Table 7.1: Holdmax and CI values for the 6 m/s and 10 m/s thresholds Threshold 6 m/s GSAT_E1_F_B_00000 Time in [ms]

0

0.5

1

1.5

2

2.5

3

3.5

4

...

10

Holdmax CI

0 0

0 0

1 1

1 2

1 3

1 4

1 5

1 6

1 7

1 ...

1 19

Threshold 10 m/s GSAT_E1_F_B_00000 Time in [ms]

0

0.5

1

1.5

2

2.5

3

3.5

4

...

10

Holdmax CI

0 0

0 0

0 0

0 0

0 0

0 0

0 0

1 1

1 2

1 ...

1 14

The 6 m/s threshold is exceeded for the first time at 1 ms. The CI value is therefore set to the value 1 at 1 ms and increases accordingly in each sampling step. At 10 ms, the CI value for this sensor at this Holdmax threshold is 19. The 10 m/s threshold is exceeded for the first time at 3.5 ms. The CI value after 10 ms is 14. If these values are compared, it can be seen that the threshold has been exceeded, and if the sampling frequency is known, the time of 3.5 ms can be de-

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Table 7.2: CI, CIT and CITn values for four sensors in the time interval up to 3 ms at a Holdmax threshold of 6 m/s CIs (LF)h 6 m/s Time in [ms]

0

0.5

1

1.5

2

2.5

3

GSAT_E1_F_B_00000 GSAT_E1_F_B_m0100 GSAT_E1_F_B_m0200 GSAT_E1_F_B_m0300

0 0 0 0

0 0 0 0

1 0 0 1

2 0 0 2

3 1 0 3

4 2 0 4

5 3 1 5

CITs (LF)h 6 m/s Time in [ms]

0

0.5

1

1.5

2

2.5

3

CIT4 (LF)6

0

0

2

4

7

10

14

CITns (LF)h 6 m/s Time in [ms]

0

0.5

1

1.5

2

2.5

3

CITn4 (LF)6

0

0

0.50

1.00

1.75

2.50

3.50

termined. For further processing in the algorithm, the CI values of the individual sensors are to be combined to form an overall value. To do so, another measure is introduced: the total crash intensity (CIT).

C I Ts (LF )h =

s1  T1 

(H ol d ma x h (Sensorsi g nal(LF )s ))(T,s)

(7.2)

s=0 T =0

With: s1

number of sensors under consideration

In order to keep the total crash intensity comparable over a variable number of sensors, it is standardized accordingly: C I T ns (L F )h =

C I Ts (LF )h s1

(7.3)

7.2 Crash Intensity as a New Input Variable for the CI Algorithm

117

Table 7.2 shows an example for CIT and CITn values for four sensors in a time interval up to 3 ms. In total over all load cases (33), all deceleration thresholds (7) and all sensors (219) evaluated in the following chapters and for all sampling times in the time interval of 300 ms, 30,403,989 data values and thus 50,589 crash intensity values are available for further analysis.

8 First Degree of Freedom: Holdmax Threshold In this chapter, a methodology is proposed to find the optimal solution from seven different Holdmax thresholds in the present application. To do so, the CI values determined in the simulation are to be used. A comprehensive comparison of the thresholds using four different criteria is carried out on the basis of proximity measures. In addition, time correlations between the triggering of sensors and the level of the Holdmax threshold are evaluated.

8.1 Methodology for Selecting the Holdmax Threshold As shown in the previous chapter, it is necessary to select the Holdmax threshold, which adjusts the sensitivity of the algorithm such that it provides a good balance between temporal requirements, separation potential between the individual load cases and good Misuse sensitivity. In order to keep the requirements on the sensor low, the threshold level should remain the same for all sensors. In the following, a methodology is presented to identify the optimal threshold for the respective vehicle application. In order to be able to compare the suitability of different thresholds, a catalogue of criteria for quality assessment was developed. Four different quality criteria are used for differentiation purposes: 1. Firing times The essential requirement on the algorithm is timely firing for all load cases included in the load case set under consideration. For the evaluation of this criterion, the fastest and the slowest load case from each of the three load case groups Wall 0°, Wall 30° and ODB are included in the evaluation. To ensure stable firing times, a decision in the algorithm should not depend on a single fast sensor. This criterion evaluates the number of sensors which have a CIT > 1 within the necessary firing time of the respective load case. For all considered sensor planes, plane 1 to plane 11, i.e. in total for all sensors, a © Springer Fachmedien Wiesbaden GmbH, part of Springer Nature 2020 A. Leschke, Algorithm Concept for Crash Detection in Passenger Cars, https://doi.org/10.1007/978-3-658-29392-5_8

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Chapter 8 First Degree of Freedom: Holdmax Threshold

threshold of the triggered sensors of at least 5% is defined as the evaluation criterion for robust timely triggering. For plane 1, this value is defined as being at least 20% and for plane 2 as being at least 10%. 2. Misuse stability In each crash algorithm, it is absolutely necessary to prevent unintentional deployment. This is due to the high repair costs for airbag deployments and the possible danger to the occupants. Therefore, robustness against a wrong decision in the algorithm is an essential component of a stable algorithm. The evaluation is based on proximity measures that describe the distance between NoFire load cases and Fire load cases. 3. Separation capability between the load case groups and total separation capability The algorithm should be capable of providing reliable information as to which load case group the present crash belongs to. The separability between the different load case groups such as Wall 0° and ODB load cases is evaluated. For the evaluation, proximity measures of the individual load case groups are evaluated over different time interpolation points. All load cases within a load case group are averaged, and an average value per load case group is calculated. In addition, the average of all load case group values is calculated, and thus a statement on the overall separation capability is made. 4. Separation capability within the load case groups The load cases within the same load case group have different time requirements but very similar load waves. For example, within the Wall 0° load case group, there are five different load cases from 27 km/h to 56 km/h which have to be separated from each other. A robust separation of these load cases from each other is therefore included in the evaluation. The evaluation is also based on proximity measures.

8.1.1 Proximity Measures To evaluate the first criterion, a statistical evaluation of the individual load cases and of the individual sensors on the respective levels is carried out. For the evaluation of criteria 2 to 4, a complex statistical procedure is used, as a multitude of data is to be compared and evaluated. A method of multivariate

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121

statistics is employed which evaluates the similarity of the data and thus the probability of a good separation of individual load cases. The more similar the load cases are with regard to the input variable of the algorithm (in this case CITn), the more difficult it is to separate them from each other. Evaluation of the similarity is performed using the proximity measure, a measure of data similarity from the statistical field of multivariate statistics. [Backhaus et al., 2016; Fahrmeir et al., 2016] Basically, there are two types of proximity measures: • Measures of similarity reflect the similarity between two objects: The greater the value of a similarity measure, the more similar two objects are to each other. • Distance measures measure the dissimilarity between two objects: The greater the distance, the more dissimilar two objects are. If two objects are to be regarded as being completely identical, then a distance of zero results. Depending on the scale level of the characteristics under consideration, there is a large number of proximity measures. A distinction is made between proximity measures for variables with metric scale level (interval), proximity measures for variables with nominal scale level (frequency data) and proximity measures for variables with binary expression (0 / 1 variable). Figure 8.1 shows a selection of the most common proximity measures. Since only equally-scaled metric variables are evaluated in the application case considered here, the analysis is based on Minkowski metrics as proposed in [Kuckartz et al., 2013] for this type of data. Minkowski metric or L-Norm: ⎡ dk,l = ⎣

J 

⎤ 1r |x k, j − x l, j | r ⎦

(8.1)

j=1

With: Dk,l xk,j , xl,j r≥1

distance between objects k and l value of variable j for object k and l (j = 1,2,...,J) Minkowski constant

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Chapter 8 First Degree of Freedom: Holdmax Threshold

Figure 8.1: Proximity measures

For further investigation, r = 2, the squared Euclidean distance, is selected because it allows an unsigned evaluation of the data. For the application case considered here, a difference between two pieces of data of +1 is equivalent to a distance of -1, since it is only a matter of evaluating the distance as a directionless quantity. Because CITn (total crash intensity normalized) as the input variable is the essential data value in the present algorithm and because the evaluation in the main algorithm is based on this value, it serves here as the basis for evaluation of the selectivity.

8.1.2 Minkowski Metrics The evaluation of criteria 2 to 4 is based on an evaluation of proximity measures according to Minkowski. Within the scope of this evaluation, the entire information content of all sensors is referenced. Therefore, the standardized crash intensity total across all sensors for the respective load cases is considered. Since the height of the proximity measures is strongly context-dependent, no absolute value can be specified for a good or a bad distance value. The evaluation of the results is therefore always carried out for the thresholds examined relative to

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123

each other. In order to be able to compare the evaluation over the course of the crash, the CITn values are normalized to the respective time grid points with the number of sampling steps within the time period considered and multiplied by 100. This generates the valuation parameter Minkowski CITn (MCITn): M C I T ns (L F )h =

C I T ns (LF )h · 100 T1

(8.2)

In the first step as shown in Table 8.1, the values for the entire simulated period up to t1 = 300 ms after crash start were evaluated. This raw data matrix, created by using the MCITn values, forms the starting point for further considerations. It contains a characteristic MCITn value per deceleration threshold for each of the 33 load cases. For these, the proximity measures according to Minkowski can be calculated. The result is a 33 x 33 value matrix in which each load case is assigned a squared Euclidean distance dimension to each other load case. The greater this value, the more dissimilar the load cases and the greater the probability that they can be discriminated against in an algorithm. Table 8.2 shows a section of the matrix calculated in this way for the 6 m/s deceleration threshold. On the basis of the data thus obtained, criteria 2 to 4 can be assessed. To evaluate criteria 3 and 4, which compare load case groups with each other, the proximity measures of the respective load case group are averaged. Only the load case group "Other" is considered separately, since the two load cases Pole and Truck are not comparable with regard to their load wave. For criterion 5, in which the selectivity within a load case group is evaluated, the individual proximity measures for each load case are compared with each other.

8.2 Determination of the Optimum Holdmax Threshold The methodology described is applied in the following chapter. In the course of a preliminary analysis, it was determined that a selection of the considered thresholds should be made within the limits of 2 m/s to 12 m/s. At a threshold

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Chapter 8 First Degree of Freedom: Holdmax Threshold

Table 8.1: MCITn for the thresholds 2 m/s to 12 m/s with t1 = 300 ms evaluation time Holdmax level [m/s] 40 km/h ODB left 56 km/h ODB left 64 km/h ODB left 40 km/h ODB right 56 km/h ODB right 64 km/h ODB right 32 km/h Wall 30° left 40 km/h Wall 30° left 32 km/h Wall 30° right 40 km/h Wall 30° right 27 km/h Wall 0° 32 km/h Wall 0° 40 km/h Wall 0° 50 km/h Wall 0° 56 km/h Wall 0° 30 km/h Truck 35 km/h Pole 10 km/h RCAR Bumper left 10 km/h RCAR Bumper center 10 km/h RCAR Bumper right 60 km/h Wildlife left 70 km/h Wildlife left 80 km/h Wildlife left 60 km/h Wildlife center 70 km/h Wildlife center 80 km/h Wildlife center 60 km/h Wildlife right 70 km/h Wildlife right 80 km/h Wildlife right 16 km/h RCAR left 16 km/h RCAR right 16 km/h RCAR left 10° 16 km/h RCAR right 10°

2

4

6

7

8

10

12

90.8 92.2 92.7 90.8 92.2 92.8 90.4 91.8 90.6 92.0 94.8 95.5 96.2 96.7 97.0 87.4 89.5 49.0 74.3 49.0 27.9 43.4 63.7 32.7 61.5 84.2 28.8 50.0 65.2 88.9 89.0 89.9 90.2

83.1 87.2 88.6 83.5 87.3 88.8 84.9 88.1 85.3 88.7 92.6 93.5 94.5 95.4 95.8 76.6 85.4 0.2 26.2 0.2 9.1 11.3 14.3 6.7 10.6 20.0 8.0 12.1 16.4 80.0 81.8 81.6 82.6

77.3 83.7 85.7 76.6 83.8 85.7 79.4 85.1 79.8 85.8 88.6 90.6 93.1 94.5 95.0 65.7 81.8 0.0 0.0 0.0 3.1 5.8 8.0 4.3 5.4 7.6 3.6 6.2 8.5 6.4 9.1 9.7 11.5

74.6 82.5 84.6 73.9 82.7 84.8 76.5 83.4 75.9 84.2 85.5 89.3 91.5 93.0 94.0 61.9 79.6 0.0 0.0 0.0 2.7 3.6 6.7 3.2 4.9 6.2 3.1 4.0 7.6 1.4 1.7 0.7 0.8

69.8 80.9 82.9 69.7 80.5 82.8 70.7 80.6 69.6 81.3 79.8 85.9 89.4 90.3 92.2 54.3 76.2 0.0 0.0 0.0 2.6 2.7 5.4 3.2 4.5 6.1 2.7 3.1 5.8 0.4 0.4 0.4 0.0

62.7 77.5 80.3 63.2 76.2 79.2 13.5 70.5 15.3 70.1 16.1 76.6 83.6 67.2 90.4 10.0 67.6 0.0 0.0 0.0 1.4 2.7 3.1 2.7 3.2 5.2 1.4 2.7 3.1 0.0 0.0 0.0 0.0

27.1 71.6 76.7 25.3 70.7 76.2 2.9 15.7 2.1 17.5 4.7 14.8 53.7 61.1 88.8 5.1 27.8 0.0 0.0 0.0 0.9 1.4 2.7 0.9 2.3 3.6 1.4 1.4 2.7 0.0 0.0 0.0 0.0

8.2 Determination of the Optimum Holdmax Threshold

125

6.5 0.1 1.9 7.2 0.0

8.4 2.0 0.1 9.1 1.9 0.0

2.1 4.4 6.4 2.8 4.4 6.3 0.0

... ... ... ... ... ... ... ... ...

16 km/h RCAR right 10°

0.7 7.1 9.1 0.0

32 km/h Wall 30° left

8.5 2.0 0.0

64 km/h ODB right

6.5 0.0

56 km/h ODB right

0.0

40 km/h ODB right

64 km/h ODB left

40 km/h ODB left 56 km/h ODB left 64 km/h ODB left 40 km/h ODB right 56 km/h ODB right 64 km/h ODB right 32 km/h Wall 30° left ... 16 km/h RCAR right 10°

56 km/h ODB left

proximity level

40 km/h ODB left

Table 8.2: Section of distance dimension matrix for the 6 m/s threshold at the 300 ms evaluation point

65.7 72.2 74.2 65.1 72.3 74.1 67.8 ... 0.0

of less than 2 m/s, most sensors trigger the lighter load cases (e.g. 40 km/h ODB) simultaneously with the heavy Misuse events (e.g. 80 km/h wildlife accident). A differentiation of Fire and NoFire load cases is therefore no longer possible. At thresholds > 12 m/s, virtually no sensors trigger, except for in the heaviest load cases. A differentiation is therefore also no longer possible. The signals are evaluated for seven different thresholds: 2 m/s, 4 m/s, 6 m/s, 7 m/s, 8 m/s, 10 m/s and 12 m/s. The thresholds are located at a distance of 2 m/s, a smaller differentiation has not proved to be effective. The 7 m/s threshold was introduced at a later stage in the course of the investigations, as the best results were obtained in the range between 6 m/s and 8 m/s.

8.2.1 Criterion 1: Firing Times For the evaluation of the first criterion, the fastest and slowest load cases from each load case group were considered. 1. 27 km/h Wall 0° and 56 km/h Wall 0° 2. 40 km/h ODB and 64 km/h ODB, both left

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Chapter 8 First Degree of Freedom: Holdmax Threshold

Figure 8.2: Comparison of number of sensors above the Holdmax threshold at 2 m/s and 10 m/s for a 64 km/h ODB left load case

3. 32 km/h Wall 30° and 40 km/h Wall 30°, both left 4. Pole and Truck In order to ensure a robust evaluation of the load cases within the time constraints, the decision should be based on as large a number of sensors as possible. These sensors must exceed their Holdmax threshold within the firing time required for the respective load case (Figure 8.2). Three sensor groupings are evaluated: Total collective of all sensors Since all planes are included in the overall sensor collective, the minimum threshold was set at > 5%. If the load waves in the rear sensor areas are considered, it is evident that only the heaviest load cases from the load spectrum lead to relevant signals. In the reduction of the number of sensors that is shown in the following chapters, most sensors are sorted out due to them not being significant for differentiation. Plane-one sensors The plane-one sensors, on the other hand, make a major contribution to CITn in almost all load cases, but also in heavy Misuse load cases. A large number of sensors is aimed for when selecting thresholds, since the first plane naturally makes the fastest contribution to the firing time during a crash. The threshold was set at > 20%.

8.2 Determination of the Optimum Holdmax Threshold

127

Table 8.3: Sensors of all planes with Holdmax threshold [m/s] exceedance Group

Load case

2

4

6

7

8

10

12

Wall 0°

56 Km/h Wall 0° 27 km/h Wall 0°

63 74

39 36

37 32

36 28

35 16

32 2

31 0

ODB

64 km/h ODB left 40 km/h ODB left

62 41

31 21

23 13

21 9

19 8

15 5

7 0

Wall 30°

40 km/h Wall 30° left 32 km/h Wall 30° left

58 63

41 36

26 19

23 11

17 7

8 2

1 0

Other

Truck Pole

82 63

39 49

17 25

11 19

9 16

6 11

5 6

Plane-two sensors For plane 2, the threshold was set at > 10%. Compared to plane 1, plane 2 is much less sensitive to Misuse but is loaded by all Fire load cases, so that it separates significantly between Fire and NoFire, but at the same time is loaded very early in the crash due to its position in the front area of the vehicle. The results of the investigation for the various load thresholds are presented below. Table 8.3 shows the evaluation of all sensors. The load thresholds from 2 m/s to 6 m/s in particular show a very good result with stable triggering of the sensors of more than 20% in almost all load cases. The 40 km/h ODB crash is conspicuous. This load case also showed a low load pattern in the associated load wave. Above the 2 m/s threshold, the number of triggered sensors decreases significantly. At the 10 m/s and 12 m/s threshold, a poor result is shown for all load cases. For the lighter load cases in the respective groups in particular, the specified 5% threshold is clearly undershot. Only the heaviest load case, the 56 km/h Wall 0° load case, continues to have a consistently high number of triggered sensors in this time window despite its extremely fast triggering time of 10 ms. On the first plane shown in Table 8.4, a stable number of sensors is triggered in all load cases from 2 m/s to 8 m/s. Especially the heavy load cases within the load case groups trigger more than 50% of these sensors which are attached to very exposed components. It is noticeable that the number of sensors triggered at the 10 m/s and 12 m/s thresholds is significantly lower and that the selected

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Chapter 8 First Degree of Freedom: Holdmax Threshold

Table 8.4: Sensors on the first plane with Holdmax threshold [m/s] exceedance Group

Load case

2

4

6

7

8

10

12

Wall 0°

56 Km/h Wall 0° 27 km/h Wall 0°

100 100

100 100

100 100

100 97

100 45

100 7

100 0

ODB

64 km/h ODB left 40 km/h ODB left

100 97

86 66

62 52

59 41

52 34

45 31

31 0

Wall 30°

40 km/h Wall 30° left 32 km/h Wall 30° left

100 100

97 86

66 55

59 31

52 24

24 0

0 0

Other

Pole Truck

97 100

79 55

69 45

52 24

48 24

28 24

3 24

Table 8.5: Sensors on the second plane with Holdmax threshold [m/s] exceedance Group

Load case

2

4

6

7

8

10

12

Wall 0°

56 Km/h Wall 0° 27 km/h Wall 0°

100 100

98 90

94 77

94 67

94 44

85 4

81 2

ODB

64 km/h ODB left 40 km/h ODB left

90 71

58 50

44 29

38 17

35 15

27 8

17 2

Wall 30°

40 km/h Wall 30° left 32 km/h Wall 30° left

100 100

81 71

54 31

46 21

29 15

19 8

4 2

Other

Pole Truck

88 92

52 46

27 17

15 13

8 13

2 9

0 8

threshold of > 20% is clearly not reached by the light load cases within the groups. This clearly indicates a lack of sensitivity. On the second plane (Table 8.5), the result is similar. The Wall 0° load cases remain very stable up to 8 m/s. The other load cases safely exceed the 10% threshold. The only exception here is the Pole load case, which misses the target at the 8 m/s threshold. However, this load case is an exception in that, as a representative of tree accidents, it is the only load case in the collective that does not provide a planar entry on the opponent of the accident. If the following planes 3 and 4 are evaluated for this load case, then it can be seen that it triggers a significant number of sensors here. Especially on plane 3, with a concentration of the components in the middle of the vehicle, > 70% of the sensors trigger at the 8 m/s threshold.

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129

On the basis of the results presented, the 10 m/s and 12 m/s thresholds are not suitable for meeting the requirements on timely firing for the load cases considered. They were therefore not taken into account in the further investigations.

8.2.2 Criterion 2: Misuse Stability To evaluate the misuse stability, the different load case groups were compared to the Misuse and NoFire load cases using the different Holdmax thresholds. In order to obtain a holistic picture of the entire crash severity progression, the evaluation takes place at various characteristic points in time during the course of the crash. For the following scenarios, the Minkowski metrics are formed from the MCITn values at the respective time grid points in the crash sequence and are related to each other: • Scenario 1: 300 ms, delimitation against slow NoFire load cases • Scenario 2: 80 ms, delimitation against fast NoFire load cases • Scenario 3: 45 ms, firing time at the limit of the slowest crashes in the load case set • Scenario 4: 20 ms, firing time between the slow Wall 0° and the fast Oblique crash scenarios • Scenario 5: 10 ms, firing time of fastest crash in load case set The scenarios presented form the basis for the evaluation of criteria 3 to 5. Evaluation of scenario 1 The first scenario is used to explain the methodology in detail. The Wall 30° load case serves as an example. Table 8.6 shows a section of the MCITn values for the evaluation of the Wall 30° and the NoFire / Misuse load cases. Based on the MCITn values shown in Table 8.6, the proximity measures between the individual Wall 30° load cases and the NoFire / Misuse load cases are determined. Table 8.7 shows the corresponding values and their averages.

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Chapter 8 First Degree of Freedom: Holdmax Threshold

Table 8.6: Section of MCITn values for Wall 30° and NoFire / Misuse load cases Holdmax Level in [m/s] 32 km/h Wall 30° left 40 km/h Wall 30° left 32 km/h Wall 30° right 40 km/h Wall 30° right 10 km/h RCAR Bumper left 10 km/h RCAR Bumper center 10 km/h RCAR Bumper right 60 km/h Wildlife left 70 km/h Wildlife left ... 16 km/h RCAR left 10° 16 km/h RCAR right 10°

2

4

6

7

8

10

12

90.4 91.8 90.6 92.0 49.0 74.3 49.0 27.9 43.4 ... 89.9 90.2

84.9 88.1 85.3 88.7 0.2 26.2 0.2 9.1 11.3 ... 81.6 82.6

79.4 85.1 79.8 85.8 0.0 0.0 0.0 3.1 5.8 ... 9.7 11.5

76.5 83.4 75.9 84.2 0.0 0.0 0.0 2.7 3.6 ... 0.7 0.8

70.7 80.6 69.6 81.3 0.0 0.0 0.0 2.6 2.7 ... 0.4 0.0

13.5 70.5 15.3 70.1 0.0 0.0 0.0 1.4 2.7 ... 0.0 0.0

2.9 15.7 2.1 17.5 0.0 0.0 0.0 0.9 1.4 ... 0.0 0.0

Table 8.7: Section of proximity measures for Wall 30° and NoFire / Misuse load cases at 2 m/s Holdmax threshold proximity level 10 km/h RCAR Bumper l. 10 km/h RCAR Bumper cen. 10 km/h RCAR Bumper r. 60 km/h Wildlife l. 70 km/h Wildlife l. 80 km/h Wildlife l. 60 km/h Wildlife cen. 70 km/h Wildlife cen. 80 km/h Wildlife cen. 60 km/h Wildlife r. 70 km/h Wildlife r. ... 16 km/h AZT l. 10° 16 km/h AZT r. 10° Average

32 km/h Wall 30° l.

40 km/h Wall 30° l.

32 km/h Wall 30° r.

40 km/h Wall 30° r.

41.40 16.08 41.35 62.50 46.97 26.72 57.65 28.84 6.22 61.56 40.40 ... 0.50 0.21

42.84 17.52 42.78 63.93 48.40 28.16 59.08 30.28 7.66 63.00 41.84 ... 1.94 1.65

41.62 16.30 41.56 62.71 47.18 26.94 57.86 29.06 6.44 61.78 40.62 ... 0.72 0.43

42.98 17.66 42.92 64.07 48.54 28.30 59.22 30.42 7.80 63.14 41.98 ... 2.08 1.79

18.27

8.2 Determination of the Optimum Holdmax Threshold

131

Table 8.8: Evaluation criterion 2, scenario 1 (300 ms) Holdmax Level in [m/s]

2

4

6

7

8

10

12

Wall 0° ODB Wall 30° Truck Pole

23.1 19.0 18.3 15.4 16.6

54.7 46.8 47.1 38.9 45.7

88.8 78.6 79.0 62.2 78.3

88.9 78.8 78.3 60.1 77.8

86.2 76.4 74.2 53.0 74.9

65.9 72.3 41.5 9.1 66.7

44.0 57.3 9.0 4.6 27.2

Average

18.5

46.6

77.4

76.8

72.9

51.1

28.4

Percent to highest value

23.9

60.3

100.0

99.3

94.3

66,0

36.7

The average value of 18.27 determined in this way is used as a comparative value for the evaluation of the NoFire / Misuse sensitivity of the Wall 30° load cases for the 2 m/s Holdmax threshold. If the same procedure is used for all other load cases, then the results shown in Table 8.8 are generated for the grid point at 300 ms. The 6 m/s threshold best meets the requirements on misuse stability. The results provided by the 7 m/s and 8 m/s thresholds are only of slightly lesser quality. With approximately 60% of the best value, the adjacent 4 m/s and 10 m/s thresholds provide a clearly poorer result. At 37%, the 12 m/s threshold is even worse. At the 12 m/s threshold, the distance between the Wall 30° load cases and the Truck load case decreases significantly when compared to the threshold with the best separating ability. With regard to the Truck load case, the 10 m/s threshold also performs significantly less well than the 6 m/s threshold. This result is even more pronounced for the 2 m/s threshold. Here, the misuse stability is 4 times worse than that of the 6 m/s threshold. On the basis of criterion 1 and given the lack of misuse stability, the 2 m/s, 10 m/s and 12 m/s thresholds are excluded from further consideration due to their not being effective. Scenarios 2 to 5 are evaluated using the same methodology. Depending on the time of observation, the corresponding MCITn values are calculated for all load cases, and the corresponding proximity measures are determined from the MCITn values. The summary of all results in the form of average values for all load cases per Holdmax threshold in this criterion is presented in Table 8.9.

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Chapter 8 First Degree of Freedom: Holdmax Threshold

Evaluation of scenarios 2 to 5 In all other scenarios, the 4 m/s threshold provides the best results. At 80 ms, the result is still similar to that of the 6 m/s threshold. As the time duration decreases, the weighting shifts more and more in the direction of the lower thresholds. The reason for this shift can be clearly seen in the analysis of the load waves. As the time intervals decrease, the majority of the sensors positioned in the rear planes are no longer included in the CIT, as they have not been reached by the load wave in these short time intervals. The difference between the NoFire / Misuse load cases, which usually trigger the front sensors, but do not cause the threshold to be exceeded in the rear sensor planes, and the Fire load cases, which trigger sensors in all planes, becomes smaller and smaller as the time interval decreases. It can be clearly seen that the CIT values decrease with the time horizon. This effect is compensated if the thresholds are low. This is because the load waves advance faster in the vehicle at lower thresholds and thus again include more sensors from the rear planes in the consideration. Therefore, low thresholds are advantageous, especially for small time intervals after the start of a crash. Therefore, the 4 m/s to 7 m/s thresholds offer a good performance over all considered time intervals. The 8 m/s threshold provides results of a lesser quality for all time intervals and offers no advantages. Comparison of all scenarios Table 8.10 shows that the 6 m/s threshold offers the most balanced results over all time points considered.

8.2.3 Criterion 3: Separability between Load Case Groups The methodological approach for criterion 3 is the same as that for criterion 2. The focus is on comparing the individual load case groups with each other. The average values of the proximity values of one load case group are again compared with those of another load case group. Table 8.11 shows the result of the analysis at 300 ms using the Holdmax thresholds 4 m/s to 8 m/s. Evaluation of scenario 1 The individual load case groups are compared with each other. The 8 m/s threshold offers the best separation capability. It is noticeable that the separation of the three scenarios with partial overlaps (Wall 30°, ODB and Pole) is

8.2 Determination of the Optimum Holdmax Threshold

133

Table 8.9: Evaluation of scenarios 2 to 5 10 ms Holdmax Level

Proximity level 4 m/s

6 m/s

7 m/s

8 m/s

Wall 0° ODB Wall 30° Truck Pole

13.3 8.0 1.5 1.2 1.8

11.1 5.3 1.2 0.8 1.2

9.8 4.5 1.1 0.7 1.1

8.5 4.0 0.9 0.6 1.0

Average

5.15

3.92

3.43

2.98

Percent to highest value

100

76

67

58

4 m/s

6 m/s

7 m/s

8 m/s

Wall 0° ODB Wall 30° Truck Pole

25.1 12.0 4.9 2.3 4.4

22.5 4.0 11.7 1.8 3.4

20.6 7.4 3.3 1.6 2.4

18.2 6.6 2.6 1.4 2.2

Average

9.73

8.69

7.07

6.17

Percent to highest value

100

89

73

63

20 ms Holdmax Level

Proximity level

45 ms Holdmax Level

Proximity level 4 m/s

6 m/s

7 m/s

8 m/s

Wall 0° ODB Wall 30° Truck Pole

57.3 17.6 16.1 6.3 13.3

51.6 13.6 12.9 4.8 10.8

46.7 12.2 10.2 4.2 8.6

40.0 10.6 7.2 3.7 7.0

Average

22.13

18.74

16.37

13.67

100

85

74

62

Percent to highest value 80 ms Holdmax Level

Proximity level 4 m/s

6 m/s

7 m/s

8 m/s

Wall 0° ODB Wall 30° Truck Pole

66.2 36.8 35.2 19.1 32.7

70.9 33.0 35.5 11.9 32.4

68.1 29.2 29.5 9.8 27.8

62.5 26.2 21.5 7.2 23.7

Average

38.00

36.75

32.88

28.22

100

97

87

74

Percent to highest value

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Chapter 8 First Degree of Freedom: Holdmax Threshold

Table 8.10: Comparison of all results for criterion 2 Holdmax level in [m/s]

4

6

7

8

300 ms 80 ms 45 ms 20 ms 10 ms

46.63 38.00 22.13 9.73 5.15

77.37 36.75 18.74 8.69 3.92

76.79 32.88 16.37 7.07 3.43

72.93 28.22 13.67 6.17 2.98

Average

24.33

29.09

27.31

24.80

Table 8.11: Evaluation criterion 3, scenario 1 (t1 = 300ms) Holdmax Level 4 m/s

Wall 0° ODB Wall 30° Truck Pole

Wall 0°

ODB

Wall 30°

Truck

Pole

average

0.00

3.98 0.00

3.81 1.19 0.00

8.91 4.92 5.10 0.00

4.49 1.21 0.84 4.42 0.00

2.59

Holdmax Level 6 m/s

Wall 0° ODB Wall 30° Truck Pole

Wall 0°

ODB

Wall 30°

Truck

Pole

average

0.00

5.10 0.00

4.93 1.95 0.00

13.30 8.20 8.38 0.00

5.27 1.79 1.46 8.03 0.00

3.89

Holdmax Level 7 m/s

Wall 0° ODB Wall 30° Truck Pole

Wall 0°

ODB

Wall 30°

Truck

Pole

average

0.00

5.07 0.00

5.34 2.37 0.00

14.39 9.33 9.06 0.00

5.54 2.25 1.90 8.85 0.00

4.27

Holdmax Level 8 m/s

Wall 0° ODB Wall 30° Truck Pole

Wall 0°

ODB

Wall 30°

Truck

Pole

average

0.00

5.14 0.00

6.09 3.10 0.00

16.61 11.73 10.64 0.00

5.67 2.93 2.70 10.94 0.00

5.04

8.2 Determination of the Optimum Holdmax Threshold

135

Table 8.12: Comparison of all results for criterion 3 Holdmax level in [m/s] t1 = 300 ms t1 = 80 ms t1 = 45 ms t1 = 20 ms t1 = 10 ms Average

4

6

7

8

2.59 14.13 7.23 3.94 2.45 6.07

3.89 18.02 6.69 3.35 1.93 6.78

4.27 18.14 6.12 3.04 1.64 6.64

5.04 17.52 5.31 2.75 1.51 6.43

clearly worse than the separation of the load cases with full overlap (Wall 0° and Truck). Table 8.12 summarises the results of the other scenarios. The comparison is made on the basis of the average values for all load case separations. Comparison of all scenarios As has been the case for criterion 2, the high thresholds provide better results in the case of long crash durations. The reason is similar to that for criterion 2, except for the fact that the load cases with the smaller load wave are not NoFire / Misuse load cases, but by the weaker crash load cases within the ODB and Wall 30° load case spectrum. Apart from this, the behaviour can be explained in the same way. Viewed as a whole across all time scenarios, the most balanced results regarding the separability of the load case groups are provided by the 6 m/s threshold.

8.2.4 Criterion 4: Separability within the Load Case Groups Criterion 4 evaluates the separability within the load case groups, as shown in Table 8.13, using Scenario 1 as an example. The Pole and Truck load cases are no longer considered, since the load case collective only contains one speed per load case. The methodology and procedure are exactly the same as for the previous criteria. Therefore, a detailed presentation of the results is omitted and, in addition to the example of the evaluation of the Wall 0° load cases at 300 ms, only the comparison of the average values shown in Table 8.14, is dealt with. Comparison of all scenarios When comparing all scenarios, the 8 m/s threshold provides the best results.

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Chapter 8 First Degree of Freedom: Holdmax Threshold

Table 8.13: Evaluation of criterion 4, partial scope of Wall 0°, scenario 1 Holdmax Level 4 m/s

27 km/h Wall 0° 32 km/h Wall 0° 40 km/h Wall 0° 50 km/h Wall 0° 56 km/h Wall 0°

27 km/h Wall 0°

32 km/h Wall 0°

40 km/h Wall 0°

50 km/h Wall 0°

56 km/h Wall 0°

0.00

0.86 0.00

1.87 1.01 0.00

2.74 1.88 0.87 0.00

3.16 2.30 1.29 0.41 0.00

average

1.09

Holdmax Level 6 m/s

27 km/h Wall 0° 32 km/h Wall 0° 40 km/h Wall 0° 50 km/h Wall 0° 56 km/h Wall 0°

27 km/h Wall 0°

32 km/h Wall 0°

40 km/h Wall 0°

50 km/h Wall 0°

56 km/h Wall 0°

0.00

2.04 0.00

4.47 2.43 0.00

5.90 3.86 1.43 0.00

6.38 4.34 1.91 0.48 0.00

average

2.21

Holdmax Level 7 m/s

27 km/h Wall 0° 32 km/h Wall 0° 40 km/h Wall 0° 50 km/h Wall 0° 56 km/h Wall 0°

27 km/h Wall 0°

32 km/h Wall 0°

40 km/h Wall 0°

50 km/h Wall 0°

56 km/h Wall 0°

0.00

3.79 0.00

5.98 2.19 0.00

7.46 3.67 1.48 0.00

8.44 4.65 2.45 0.97 0.00

average

2.74

Holdmax Level 8 m/s

27 km/h Wall 0° 32 km/h Wall 0° 40 km/h Wall 0° 50 km/h Wall 0° 56 km/h Wall 0°

27 km/h Wall 0°

32 km/h Wall 0°

40 km/h Wall 0°

50 km/h Wall 0°

56 km/h Wall 0°

0.00

6.09 0.00

9.59 3.50 0.00

10.53 4.45 0.94 0.00

12.38 6.30 2.79 1.85 0.00

average

3.90

8.2 Determination of the Optimum Holdmax Threshold

137

Table 8.14: Comparison of all results for criterion 4 Holdmax level in [m/s]

4

6

7

8

t1 = 300 ms

Wall 0° Wall 30° ODB Average

1.09 1.45 2.09 1.54

2.21 2.45 3.38 2.68

2.74 3.20 4.02 3.32

3.90 4.49 5.01 4.47

t1 = 80 ms

Wall 0° Wall 30° ODB Average

4.08 8.11 7.48 6.56

6.35 8.11 11.16 8.54

7.16 10.00 11.45 9.54

10.76 11.11 12.13 11.33

t1 = 45 ms

Wall 0° Wall 30° ODB Average

7.22 2.23 3.81 4.42

10.61 3.04 3.47 5.71

11.75 3.25 3.54 6.18

14.79 2.84 3.67 7.10

t1 = 20 ms

Wall 0° Wall 30° ODB Average

7.13 1.20 2.09 3.47

6.95 1.17 2.07 3.40

6.95 0.87 1.95 3.26

8.55 1.01 1.90 3.82

t1 = 10 ms

Wall 0° Wall 30° ODB Average

4.59 0.31 1.55 2.15

4.66 0.35 1.46 2.16

4.36 0.29 1.29 1.98

5.03 0.37 1.27 2.22

Average overall

3.63

4.50

4.85

5.79

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Chapter 8 First Degree of Freedom: Holdmax Threshold

Table 8.15: Overview of the overall result Holdmax level [m/s]

Criterion 1: (ok/all) 2 to 4: (prox. level) All planes 1

3

4

4

6

7

8

10

12

8/8

8/8

8/8

8/8

8/8

5/8

3/8

Sensors plane 1

8/8

8/8

8/8

8/8

8/8

6/8

3/8

Sensors plane 2

8/8

8/8

8/8

8/8

7/8

4/8

2/8

51.08

28.42

Scen. 1 (300 ms)

2

2

46.63

77.37

76.79

72.93

Scen. 2 (80 ms)

18.50

38.00

36.75

32.88

28.22

Scen. 3 (45 ms)

22.13

18.74

16.37

13.67

Scen. 4 (20 ms)

9.73

8.68

7.07

6.17

Scen. 5 ( 10 ms)

5.15

3.92

3.43

2.98

Scen. 1 (300 ms)

2.59

3.89

4.27

5.05

Scen. 2 (80 ms)

14.13

18.02

18.14

17.52

Scen. 3 (45 ms)

7.23

6.69

6.12

5.31

Scen. 4 (20 ms)

3.94

3.35

3.04

2.75

Scen. 5 ( 10 ms)

2.45

1.93

1.64

1.51

Scen. 1 (300 ms)

1.54

2.68

3.32

4.47

Scen. 2 (80 ms)

6.56

8.54

9.54

11.33

Scen. 3 (45 ms)

4.42

5.71

6.18

7.10

Scen. 4 (20 ms)

3.47

3.40

3.26

3.82

Scen. 5 ( 10 ms)

2.15

2.16

1.98

2.22

However, the quality of results shifts for shorter observation periods. This can be seen particularly clearly in load cases with a lesser load on the front end.

8.2.5 Summary Table 8.15 shows the results of the individual criteria for all scenarios. A colour scale is used to present the evaluation of the results. As has been shown at the beginning of the chapter, there is no absolute value for a "good" rating.

8.2 Determination of the Optimum Holdmax Threshold

139

The individual results are therefore rated relative to each other. Deviations of 20% to 30% from the best value are shown in yellow and deviations greater than 30% in red. Overall, the 6 m/s threshold provides the most balanced result across all criteria and time scenarios considered. The 7 m/s threshold also provides a good overall result. When comparing these two thresholds, the decisive factor is criterion 2, the misuse stability. Here, the 6 m/s threshold provides the better result across all scenarios. For criteria 3 and 4, the results are approximately the same. However, the 6 m/s threshold performs better for the more relevant scenarios 3 to 5 and thus with regard to the firing times of the Fire load cases. Although the low 2 m/s threshold fulfils the time requirements very well, it shows great weaknesses with regard to misuse stability and should therefore not be used. The 4 m/s threshold shows slight weaknesses with regard to misuse stability but above all a poor result with regard to the separation between and among load cases. The 8 m/s threshold shows great weaknesses especially with regard to misuse stability. The high 10 m/s and 12 m/s thresholds do not sufficiently meet the time requirements. For these reasons, these thresholds are not appropriate. The application of the methodology described in Section 8.1 shows that, for the vehicle examined here, the use of the 6 m/s threshold is the most appropriate solution.

9 Data Duality of Crash Intensity Values In this chapter a methodology is presented with the help of which an evaluation of the data quality of the CI values can be carried out. The aim is to make a statement as to whether the available data can be used to create an application that is suitable to meet the requirements on an algorithm which can be used to reach a decision on the firing time. For this purpose, a methodology of multivariate statistics is used to assess whether the data allow sufficient selectivity between NoFire / Misuse and Fire load cases. In another methodology, it is determined whether a desired relationship between firing time requirement and CIT value is established for each load case under consideration.

9.1 Model for Assessing the Selectivity between the Load Case Groups Based on the optimum Holdmax threshold as determined in the previous chapter and on the proposed geometric sensor distribution, the quality of the CI and CIT data will be evaluated in the following. The main question here is whether the data provide enough information content to meet all requirements of an algorithm which is based on this data. To this end, the classifiability of the evaluation variables is to be investigated using cluster analysis, a method of multivariate statistics, and the relationships between the individual load cases and their CIT values are to be presented over the course of the crash. With the help of cluster analysis, large amounts of data can be analysed in order to recognize similarities and correlations in them. Thus, the suitability of the data of the different load cases for the classification of load case groups and for the differentiation between individual load cases can be evaluated. The proximity measures introduced in the previous chapter form the data basis for the following analysis methods. Matrices are formed from the totality of the CI values per load case. These matrices serve as a starting point for the cluster algorithms. Various algorithms are available for data fusion in a cluster

© Springer Fachmedien Wiesbaden GmbH, part of Springer Nature 2020 A. Leschke, Algorithm Concept for Crash Detection in Passenger Cars, https://doi.org/10.1007/978-3-658-29392-5_9

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Chapter 9 Data Duality of Crash Intensity Values

Figure 9.1: Overview of selected cluster methods [Backhaus et al., 2016]

analysis. These algorithms mainly differ with regard to the type of fusion process between similar load cases. The great advantage of cluster analysis is that it not only classifies on the basis of a single descriptive feature, such as the CIT, but also simultaneously uses all relevant descriptive features (here the CI values of the individual sensor measuring points) to group the load cases. Figure 9.1 shows a selection of known fusion processes. The selection of the cluster method is essentially determined by the type of input data and by the objective of clustering in terms of homogeneity or number. In partitioning procedures, a fixed number of clusters is specified, and the corresponding load cases are then assigned to the clusters in the course of the procedure. In hierarchical procedures, the number of clusters is calculated at the end of the evaluation process. If the assessment is carried out agglomeratively, then the process starts with the total number of load cases considered as individual groups and then iteratively merges the load cases that are most

9.1 Model for Assessing the Selectivity

143

similar according to certain criteria. The hierarchical agglomerative approach is most suitable for the analysis applied, since the question of which load cases are included in which groups at the end of the fusion process is the most important. [Backhaus et al., 2016] The fusion algorithm to be used shall be determined prior to the commencement of the procedure. These algorithms differ essentially in the methodology used to evaluate similarities between groups. In addition to methods such as Single Linkage or the Centroid method, which are based on a mere evaluation of the distance between groups, the Ward method, which focuses on minimizing the variance, offers clear advantages for the present application case. The Ward method mainly differs from methods which solely use a distance measurement approach in that it does not combine the groups with the smallest distance. Instead, objects (groups) are combined, which least increase a given heterogeneity measure. The aim of the Ward method therefore is to unite those objects (groups) that increase the variance in a group as little as possible. In this way, clusters are formed which are as homogeneous as possible: in our application, load case groups such as Wall 0°, Wall 30°, ODB and NoFire load cases. The variance criterion, also known as the error square sum, is used as the heterogeneity measure. [Kuckartz et al., 2013] Clustering results in a dendrogram in which the clustering of the load cases into groups is displayed graphically. There are also various methods for determining the optimal number of clusters. The identification of the so-called elbow in the change of the heterogeneity measure in the scree plot can be used as a first evaluation criterion for initial assessment. [Kuckartz et al., 2013] For a more precise evaluation of the quality of cluster separation, various other methods are given in the literature. In a study by Milligan and Cooper [Milligan and Cooper, 1985], which assessed the various procedures, the Mojena procedure [Mojena, 1977] received a positive rating. The result of the Mojena procedure, ˜i (Mojena coefficient) enables different the standardized fusion coefficient a cluster solutions to be compared with the help of one measure. It is therefore used for evaluation in this use case. The measure is calculated as follows: 1  ai n − 1 i=1 n−1

a˜ =

(9.1)

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Chapter 9 Data Duality of Crash Intensity Values

Diagram 9.1: Example of a dendrogram

 n−1

1  sa = (ai − a˜)2 n − 2 i=1 a˜i =

ai − a˜ sa

(9.2)

(9.3)

With: ˜i a ai n

Standardized fusion coefficient (mojena coefficient) Distance (error sum of squares) Number of load cases under consideration

˜i = 2.75. In the In his study, Mojena achieved the best results with a value of a ˜i = 1.25 is stated as being a good value. These study of Milligan and Cooper a can only be indications, since the optimal parameter depends strongly on the existing data structure. In the application case of this work and based on the ˜i ≥ 1.3 provided a fusion results and on the known data structure, values of a very good result in line with clustering expectations. The result of the cluster analysis can be visualized in a dendrogram. It shows the respective cluster in a tree diagram for each run at the distance level, in this case the error square sum (Diagram 9.1).

9.2 Evaluation of Selectivity for the 6 m/s Holdmax Threshold

145

Diagram 9.2: Selection of the best clustering according to the "elbow" criterion

In the example shown in Diagram 9.2, the optimal number of clusters is two clusters. The calculation according to Mojena provides a two-dimensional data matrix from which the optimal solution can be derived on the basis of the limits described.

9.2 Evaluation of Selectivity for the 6 m/s Holdmax Threshold An assessment as to whether the available data indicate sufficient separation capability between the load cases can be obtained from an analysis of all load cases and over the entire simulation period of 300 ms. If cluster analysis and Mojena evaluation are performed for the 33 load cases at the selected Holdmax threshold of 6 m/s, the data shown in Table 9.1 are generated. It can be seen that the solution with two clusters leads to a separation of the ˜i ≥ 1,3 two clusters at a very high level, significantly above the threshold of a which had been determined using the Mojena criterion. The scree plot in Diagram 9.3 shows a very pronounced elbow. The result indicates two load case groups that can be separated very easily. If the result

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Chapter 9 Data Duality of Crash Intensity Values

Table 9.1: Error square sums according to Ward and evaluation according to Mojena for 300 ms Number of clusters

Error sum of squares

Mojena citeria

Number of clusters

Error sum of squares

Mojena criteria

32 31 30 29 28 27 26 25 24 23 22 21 20 19 18 17

0.00 0.00 0.00 39.80 182.95 187.26 484.42 587.58 592.68 700.08 760.86 825.08 913.34 918.68 960.73 967.67

-0.5062 -0.5062 -0.4947 -0.4537 -0.4524 -0.3672 -0.3376 -0.3361 -0.3053 -0.2878 -0.2694 -0.2441 -0.2426 -0.2305 -0.2285 -0.2132

16 15 14 13 12 11 10 9 8 7 6 5 4 3 2 1

1,020.83 1,078.15 1,114.60 1,274.82 1,277.73 1,282.60 1,416.75 1,453.79 1,920.50 2,135.73 2,184.42 2,430.17 2,928.76 3,075.07 3,538.00 20,195.35

-0.197 -0.186 -0.140 -0.140 -0.138 -0.100 -0.089 0.045 0.107 0.121 0.191 0.334 0.376 0.509 5.289

Diagram 9.3: Scree plot 300 ms

9.2 Evaluation of Selectivity for the 6 m/s Holdmax Threshold

147

Diagram 9.4: Two cluster solution for 300 ms

is mapped in a dendrogram (Diagram 9.4), then it can be seen that the complete separation between the Fire and NoFire load cases is very well mapped with the methodology of local deceleration measurement at the considered 6 m/s threshold. Both clusters separate at a very high distance matrix level. One of the main challenges of crash algorithms, the robust separation capability between load cases with and without restraint system firing, is met very well here. The available data suggest very good separation capability in an algorithm.

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Chapter 9 Data Duality of Crash Intensity Values

If the same procedure is used to only consider the Fire scenarios, then a separation of the load case groups that fully corresponds to the desired result, occurs. Table 9.2: Error square sums according to Ward and evaluation according to Mojena for 300 ms Fire load cases Number of clusters

Error sum of squares

Mojena criteria

16 15 14 13 12 11 10 9 8 7 6 5 4 3 2 1

0 0.98 3.43 3.60 3.61 4.73 6.35 7.22 7.64 9.94 9.98 14.21 14.91 19.99 24.27 40.99

-0.638 -0.478 -0.467 -0.467 -0.393 -0.287 -0.230 -0.203 -0.052 -0.050 0.227 0.272 0.605 0.885 1.979

Table 9.2 shows that the two-cluster solutions enable separation at a high distance dimension level. Diagram 9.5 shows that, in the two-cluster solution, the "fast" Wall 0° load cases with their high temporal demands separate very well from the rest. When evaluating the quality of the available crash intensity values, it could be seen that the data show a high separation capability between NoFire and Fire load cases and that, when considering the Fire load cases among themselves, load cases with fast and slow crash severity progress can be separated well from each other. The data thus indicate very good suitability for fulfilling the requirements on a crash algorithm.

9.3 Methodology for Evaluating the Data Correlations In a crash algorithm, the dynamic behaviour of the algorithm input variables over time is in the foreground. The analysis of the data after the end of the

9.3 Methodology for Evaluating the Data Correlations

149

Diagram 9.5: Two cluster solution 300 ms Fire load cases

crash sequence as shown in Section 9.2 provides a good impression of the basic data quality. However, it is not suitable for enabling an adequate assessment of the data over the entire time sequence for the various crash load cases. Rather, it must be ensured for each of the load cases under consideration that it can be distinguished from all other load cases at the time when a firing decision is required for it. A key criterion for enabling the timely firing of restraint systems using just one measurement variable is to have a stable correlation between the time requirements and the magnitude of the measurement variable. The required behaviour is shown in Table 9.3. The shorter the firing time, the greater the decision criterion, in this case the CIT, should be. If load cases with a lower firing time requirement have a lower CIT than load cases with longer firing times, then the algorithm cannot be applied in a single step. Figure 9.2 shows an ideal arrangement of input data for a threshold-based algorithm. The load case with the fastest firing time requirement always has a greater CIT value than all other load cases. Deviating from the ideal behaviour shown, there are two further states which, in a threshold-based algorithm, can also lead to a result that fulfils the requirements.

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Chapter 9 Data Duality of Crash Intensity Values

Table 9.3: Monotonically decreasing relationship between firing time and CIT Load case

FTR [ms]

56 km/h Wall 0° 50 km/h Wall 0° 40 km/h Wall 0° 32 km/h Wall 0° 27 km/h Wall 0° 64 km/h 40% ODB 40 km/h 40% ODB 35 km/h Pole 40 km/h Wall 30° 32 km/h Wall 30° 30 km/h Truck

10 13 16 17 20 28 31 41 32 39 51

CIT High

Middle

Low

1111

Figure 9.2: Ideal correlation between Firing Time Requirement and CIT

9.4 Evaluation of CIT Values in Relation to Firing Time Requirements

151

Figure 9.3: First less than ideal but acceptable correlation between Firing Time Requirement and CIT

Figure 9.3 shows that load case 2 with the lesser firing time requirement up to t1 has a greater CIT than load case 1 with the greater firing time requirement. This does not lead to any problem in the algorithm, since the CIT ratio has reversed again when both load cases exceeded the threshold. The temporary nonmonotonic relationship between the two variables therefore has no effect. Figure 9.4 shows a second behaviour that is also acceptable. In this case, the undesired relationship between the two variables does not exist until time t1 , i.e. after both load cases have made their firing decision on the basis of the threshold shown. Here, too, the behaviour therefore remains without effect. All other possible constellations lead to the fact that a threshold-based algorithm such as the CI algorithm can no longer be applied according to requirements. These states must therefore be avoided and, if necessary, corrected by selecting the sensors from the entire sensor spectrum.

9.4 Evaluation of CIT Values in Relation to Firing Time Requirements As shown, it is essential for a single-stage algorithm that the load case with the fastest firing time also has the greatest CIT value at the time when a decision is needed. Faster firing time requirements should always correlate with increasing

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Chapter 9 Data Duality of Crash Intensity Values

Figure 9.4: Second less than ideal but acceptable correlation between Firing Time Requirement and CIT

crash intensity values. In the following, this behaviour is evaluated for the 219 sensor positions which had been determined geometrically. The evaluation uses the selected 6 m/s Holdmax threshold. Diagram 9.6 shows the behaviour of the 219 sensors. This evaluation is also carried out dynamically over the course of time for each load case at the time of a required firing time decision. It is important here that each load case distinguishes itself as well as possible from all slower load cases at the time of its firing decision. If slower load cases have higher CIT values at the time of the decision, then a timely triggering of the restraint system is not possible. The algorithm cannot be applied adequately. The three acceptable states described in Section 9.3 must be complied with over all load cases and over the entire time horizon. Diagram 9.6 indicates that many of the load cases show the required behaviour. If, however, the behaviour is considered in detail, as shown in Diagram 9.7, overlaps occur between individual load cases. For these overlaps, it must be analysed how this behaviour is related to the respective firing time requirements. If the three desired states are complied with here, then there is no need for action. If not, then improvements must be made by selecting sensors accordingly. In order to systematically analyse the behaviour of all load cases, it is necessary to consider each load case at its respective required firing time. To establish

9.4 Evaluation of CIT Values in Relation to Firing Time Requirements

153

Diagram 9.6: CITn values for 33 load cases and 219 sensors

Diagram 9.7: CITn values for 33 load cases and 219 sensors and optimization potential

154

Chapter 9 Data Duality of Crash Intensity Values

comparability over the different time periods considered and to include the number of sensors, the analysis is based on the MCITn values. For the 17 Fire load cases contained in the load case spectrum, this results in 11 points in time (some load cases have the same firing time requirements) which must be considered. This is shown in Table 9.4 using the firing time requirement of 10 ms (56 km/h Wall 0° load case) as an example. Table 9.4: MCITn values at 10 ms for 219 sensors FTR [ms] 10 13 16 17 20 28 28 32 32 35 35 39 39 41 41 41 51

-

Load case

MCITn

56 km/h wall 0° 50 km/h wall 0° 40 km/h wall 0° 32 km/h wall 0° 27 km/h wall 0° 64 km/h ODB left 64 km/h ODB right 40 km/h wall 30° left 40 km/h wall 30° right 56 km/h ODB left 56 km/h ODB right 32 km/h wall 30° left 32 km/h wall 30° right 40 km/h ODB left 40 km/h ODB right 35 km/h pole 30 km/h truck

3.94 3.41 2.45 1.51 1.22 1.62 1.66 0.37 0.37 1.36 1.36 0.19 0.19 0.86 0.83 0.32 0.00

80 km/h heavy animal left 70 km/h heavy animal left 60 km/h heavy animal left 80 km/h heavy animal center 70 km/h heavy animal center 60 km/h heavy animal center 80 km/h heavy animal right 70 km/h heavy animal right 60 km/h heavy animal right 16 km/h AZT left 16 km/h AZT right 16 km/h AZT left 10° 16 km/h AZT right 10° 10 km/h RCAR Bumper left 10 km/h RCAR Bumper center 10 km/h RCAR Bumper right

0.56 0.44 0.38 0.57 0.45 0.42 0.59 0.46 0.39 0.00 0.00 0.00 0.00 0.00 0.00 0.00

9.5 Summary

155

For the 56 km/h Wall 0° load case, the requirements are fulfilled. Its MCITn value of 3.94 is greater than that of all other load cases. A triggering threshold could therefore be applied. The MCITn values of all other load cases are acceptable. The value of the 56 km/h ODB load case, for example, is significantly greater than that of the 40 km/h Wall 30° load case, but both are below their required release time. The non-monotonically decreasing relationship between firing time and MCITn must therefore be tolerated, as it would not lead to any false triggering over time. The load cases with firing time requirements up to 28 ms show a similar behaviour. As shown in Table 9.5, the first behaviour that does not fulfil the requirements is found for the 40 km/h Wall 30° load cases at 32 ms. At this point, a clear firing decision must be made for these two load cases. However, the 56 km/h ODB load cases, which should only fire 3 ms later, have a higher MCITn value. A correct timely separation of both load case pairs using their corresponding MCITn values is not possible, and a false firing decision would be the result. The same problem occurs with the load cases that must fire at 39 ms. As Table 9.6 shows, the 32 km/h Wall 30° load cases cannot be separated from the 40 km/h ODB load cases and the 35 km/h Pole load case. All other load cases can be separated very well. It is noticeable that the Misuse / NoFire load cases can be easily separated from the Fire load cases over the entire observation period. This behaviour again confirms the results of the cluster analysis found in Section 9.3.

9.5 Summary In this chapter, the information pool provided by the 219 sensors, which had been distributed geometrically in the vehicle as described in Chapter 6, was evaluated with regard to its suitability in an algorithm concept for a local component load. The result of the cluster analysis clearly shows that the generated data have great potential to fulfil the requirements on a firing algorithm. The detailed analysis of the MCITn values over all load cases in their temporal course shows that a very good result can be achieved for many load cases without further

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Chapter 9 Data Duality of Crash Intensity Values

Table 9.5: MCITn values at 32 ms FTR [ms]

Load case

MCITn

10 13 16 17 20 28 28

56 km/h wall 0° 50 km/h wall 0° 40 km/h wall 0° 32 km/h wall 0° 27 km/h wall 0° 64 km/h ODB left 64 km/h ODB right

34.75 31.91 26.17 18.19 14.68 10.42 10.52

32 32 35 35

40 km/h wall 30° left 40 km/h wall 30° right 56 km/h ODB left 56 km/h ODB right

8.74 8.74 8.85 8.75

39 39 41 41 41 51

32 km/h wall 30° left 32 km/h wall 30° right 40 km/h ODB left 40 km/h ODB right 35 km/h pole 30 km/h truck

5.00 5.07 5.90 5.72 5.31 2.86

80 km/h heavy animal left 70 km/h heavy animal left 60 km/h heavy animal left 80 km/h heavy animal center 70 km/h heavy animal center 60 km/h heavy animal center 80 km/h heavy animal right 70 km/h heavy animal right 60 km/h heavy animal right 16 km/h AZT left 16 km/h AZT right 16 km/h AZT left 10° 16 km/h AZT right 10° 10 km/h RCAR Bumper left 10 km/h RCAR Bumper center 10 km/h RCAR Bumper right

4.09 3.02 1.76 3.93 2.81 2.16 4.33 3.18 1.97 0.17 0.22 0.31 0.31 0.00 0.00 0.00

-

9.5 Summary

157

Table 9.6: MCITn values at 39 ms FTR [ms]

Load case

MCITn

10 13 16 17 20 28 28 32 32 35 35

56 km/h wall 0° 50 km/h wall 0° 40 km/h wall 0° 32 km/h wall 0° 27 km/h wall 0° 64 km/h ODB left 64 km/h ODB right 40 km/h wall 30° left 40 km/h wall 30° right 56 km/h ODB left 56 km/h ODB right

48.71 45.83 39.79 29.84 21.36 14.10 14.07 12.92 12.92 12.04 11.92

39 39 41 41 41

32 km/h wall 30° left 32 km/h wall 30° right 40 km/h ODB left 40 km/h ODB right 35 km/h pole

7.53 7.93 7.86 7.64 8.02

51

30 km/h truck

4.49

80 km/h heavy animal left 70 km/h heavy animal left 60 km/h heavy animal left 80 km/h heavy animal center 70 km/h heavy animal center 60 km/h heavy animal center 80 km/h heavy animal right 70 km/h heavy animal right 60 km/h heavy animal right 16 km/h AZT left 16 km/h AZT right 16 km/h AZT left 10° 16 km/h AZT right 10° 10 km/h RCAR Bumper left 10 km/h RCAR Bumper center 10 km/h RCAR Bumper right

5.24 3.85 2.21 5.01 3.58 2.74 5.54 4.08 2.48 0.36 0.42 0.57 0.49 0.00 0.00 0.00

-

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Chapter 9 Data Duality of Crash Intensity Values

optimization. However, some points in time and load cases with unwanted behaviour are found, for which a solution must be found by optimizing the number of sensors used. Table 9.7 summarises the fields of action. Table 9.7: MCITn values for all load cases with 219 sensors MCITn / [ms]

LC 10

13

16

17

20

28

32

35

39

41

51

56W 50W 40W 32W 27W 64Ol 64Or

3.9 3.4 2.4 1.5 1.2 1.6 1.7

6.3 5.6 4.4 2.8 2.0 2.5 2.5

9.2 8.3 6.6 4.6 3.3 3.5 3.6

10.1 9.2 7.4 5.3 3.8 3.9 4.0

13.9 12.1 10.0 7.5 5.6 5.0 5.1

27.1 24.5 18.8 14.5 11.3 8.5 8.6

34.7 31.9 26.2 18.2 14.7 10.4 10.5

40.7 37.9 32.0 22.1 17.3 11.9 12.0

48.7 45.8 39.8 29.8 21.4 14.1 14.1

52.7 49.8 43.7 33.8 24.8 15.4 15.2

72.7 69.8 63.5 53.5 44.3 23.6 24.5

40Wl 40Wr 56Or 56Or

0.4 0.4 1.4 1.4

0.8 0.8 2.1 2.1

1.5 1.5 3.0 3.0

1.7 1.7 3.3 3.3

2.8 2.8 4.2 4.2

6.6 6.6 7.2 7.1

8.7 8.7 8.8 8.7

10.5 10.5 10.2 10.1

12.9 12.9 12.0 11.9

14.3 14.3 13.0 12.9

22.9 22.9 18.8 18.7

32Wl 32Wr 40Ol 40Or 35P

0.2 0.2 0.9 0.8 0.3

0.5 0.5 1.4 1.4 0.7

0.9 0.9 2.0 2.0 1.2

1.0 1.0 2.3 2.2 1.4

1.6 1.6 2.9 2.8 2.0

3.7 3.7 4.8 4.7 3.9

5.0 5.1 5.9 5.7 5.3

6.1 6.3 6.7 6.5 6.4

7.5 7.9 7.9 7.6 8.0

8.3 8.8 8.5 8.3 9.0

12.7 13.3 12.2 12.0 17.1

30T

0.0

0.1

0.2

0.3

0.7

2.0

2.9

3.5

4.5

5.0

7.8

NM

0.6 0.4 0.4 0.6 0.5 0.4 0.6 0.5 0.4 0.0 0.0 0.0 0.0 0.0 0.0 0.0

1.0 0.8 0.5 1.0 0.7 0.6 1.1 0.8 0.6 0.0 0.0 0.0 0.0 0.0 0.0 0.0

1.5 1.1 0.7 1.4 1.1 0.8 1.6 1.2 0.8 0.0 0.0 0.0 0.0 0.0 0.0 0.0

1.6 1.2 0.8 1.6 1.2 0.9 1.7 1.3 0.9 0.0 0.0 0.0 0.0 0.0 0.0 0.0

2.1 1.6 1.0 2.1 1.5 1.2 2.2 1.6 1.1 0.0 0.0 0.0 0.1 0.0 0.0 0.0

3.4 2.5 1.5 3.3 2.4 1.8 3.6 2.7 1.7 0.1 0.1 0.2 0.2 0.0 0.0 0.0

4.1 3.0 1.8 3.9 2.8 2.2 4.3 3.2 2.0 0.2 0.2 0.3 0.3 0.0 0.0 0.0

4.6 3.4 2.0 4.4 3.1 2.4 4.8 3.6 2.2 0.3 0.3 0.4 0.4 0.0 0.0 0.0

5.2 3.9 2.2 5.0 3.6 2.7 5.5 4.1 2.5 0.4 0.4 0.6 0.5 0.0 0.0 0.0

5.6 4.1 2.3 5.3 3.8 2.9 5.9 4.3 2.6 0.4 0.5 0.6 0.5 0.0 0.0 0.0

7.2 5.3 3.0 6.9 4.9 3.7 7.6 5.6 3.4 0.7 0.7 1.0 0.9 0.0 0.0 0.0

10 Second Degree of Freedom: Selection of Sensors In the previous chapter, it became clear that, in the case of purely symmetrical and grid-related sensor positioning, some load cases lead to MCITn values that do not correlate with the firing times. This skewness in the data can be influenced by a targeted selection of sensors. With regard to sensor selection, another aspect that can be considered and influenced is the question whether there are sensors that correlate so strongly that they will not make a significant contribution to the selectivity within an algorithm.

10.1 Methodology for the Reduction of Strongly Correlating Sensors The sensors positioned on all essential components in the front end of the vehicle as described in Chapter 6 were distributed in a geometrically structured grid with a narrow grid spacing. Many sensors are located in the immediate vicinity of each other and are often positioned on the same components. Due to this arrangement, it is to be expected that the measurement results of the individual sensors will partly correlate with each other. Since the use of strongly correlating measurement signals does not contribute to the improvement of the signal pool for the purpose of this task, they can be removed from the measurement data pool without any major retroactive effects. The interdependence of the data can be evaluated with a correlation analysis. As has already been shown in Section 8.1, the data considered here are interval-scaled. A Pearson rating is the solution proposed in the literature. [Hauke and Kossowski, 2011; Pearson, 1920] First, the covariance is calculated and then normalized to a productmoment correlation. Covariance is the reciprocal variance of two variables. It is obtained by first determining for each pair of values how far the x-value and the y-value are from the respective mean value. If these distances are multiplied, then the cross product is generated for each load case. These cross products are all summed up and finally divided by the number of load cases minus one: © Springer Fachmedien Wiesbaden GmbH, part of Springer Nature 2020 A. Leschke, Algorithm Concept for Crash Detection in Passenger Cars, https://doi.org/10.1007/978-3-658-29392-5_10

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Chapter 10 Second Degree of Freedom: Selection of Sensors

n cov(x, y) =

i=1 (x i

− x¯ ) · ( yi − ¯y ) n−1

(10.1)

With: xi and yi ¯ and ¯y x n

value of a load case for sensor x and sensor y average value of the sensors x and y number of load cases

However, covariance has one major disadvantage: It is dependent on the scale of the variables. When the amount of the variable values is doubled, the deviations and covariance quadruple. This means that it is not easy to compare two different covariances, which is why they are standardized in the next step. Standardization of the covariance is achieved by the calculation of the product moment correlation, which goes back to Auguste Bravais and Karl Pearson and is often called the Pearson Correlation. [Pearson, 1920] Pearson’s "r" is calculated by dividing the covariance by the product of the standard deviations of the two variables x and y:

Pearson

r=

cov(x, y) sx · s y

(10.2)

With: cov(x,y) sx and sy

covariance of the variables x and y standard deviations of the variables x and y

The correlation coefficient r can take values between -1 and +1. At r = -1, there is a perfectly negative (the more, the less) and at r = +1 there is a perfectly positive (the more, the more) linear correlation between the two variables examined, and at r = 0 there is no correlation, i.e. the variables do not correlate with each other. In other words, the sign of r indicates the direction of the correlation and the amount of r the strength of the correlation. [Spearman, 1904; Urban and Mayerl, 2018; Fahrmeir et al., 2016] In order to evaluate the correlation between the different sensors, it is necessary to consider different time periods. As described in the previous chapters, MCITn values vary considerably across time periods. In order to take these

10.2 Reduction of the Sensor Pool by Strongly Correlating Sensors

161

Table 10.1: Classification of the Pearson coefficient according to the correlation between data [Kuckartz et al., 2013] Amount of r

Strength of correlation

0.00 ≤ r < 0.10 0.10 ≤ r < 0.30 0.30 ≤ r < 0.50 0.50 ≤ r < 0.70 0.70 ≤ r < 1.00

no correlation low correlation medium correlation high correlation very high correlation

variations into account and make a balanced decision, the Pearson correlations are calculated for each of the time points shown in Table 3.4, and an equally weighted mean is then calculated. This mean value is used in the following to assess the sensor correlation. As shown in Table 10.1, the strength of the correlation can be evaluated based on the amount of the Pearson coefficient. In this application case, there naturally is a more or less pronounced correlation between all sensors. All sensors are installed in a mechanically connected front end in which there is always a correlation between the level of the measurement signals of the individual sensors and the load caused by the individual load cases. In order to minimize the number of sensors, only sensors with a Pearson value of r > 0.9 are considered correlated in the sense of the application. Starting from the first sensor plane in x-direction in the car, all sensors with a correlation of r > 0.9 are therefore removed from the sensor pool.

10.2 Reduction of the Sensor Pool by Strongly Correlating Sensors If the methodology shown in the previous chapter is applied to the present sensor collective, a matrix of the Pearson value r is generated for each time observed. Table 10.2 shows this as an example for the observation horizon of 300 ms. If the matrices are calculated for all relevant times and if the mean value is calculated as described, then the matrix shown in Table 10.3 is generated. From it, the strongly correlating sensors can be determined and removed from the sensor pool. Table 10.3 shows that sensor GSAT_E1_F_B_m0100 with a Pearson factor of 0.95 correlates with sensor GSAT_E1_F_B_m0200. Sensor GSAT_E1_F_B_m0200

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Chapter 10 Second Degree of Freedom: Selection of Sensors

Table 10.2: Section of the Pearson coefficient matrix at 300 ms

GSAT_E1_F_B_m0400

GSAT_E1_F_B_m0500

0.78 0.94 -

0.72 0.88 0.94 -

0.67 0.84 0.90 0.97 -

0.67 0.84 0.90 0.97 1.00 -

... ... ... ... ... ... -

GSAT_E11_SW_T_p0400

GSAT_E1_F_B_m0300

0.84 -

GSAT_E11_SW_T_p0200

GSAT_E1_F_B_m0200

-

...

GSAT_E1_F_B_m0100

GSAT_E1_F_B_00000 GSAT_E1_F_B_m0100 GSAT_E1_F_B_m0200 GSAT_E1_F_B_m0300 GSAT_E1_F_B_m0400 GSAT_E1_F_B_m0500 ... GSAT_E11_SW_T_p0200 GSAT_E11_SW_T_p0400

GSAT_E1_F_B_00000

Pearson Coefficient

0.70 0.72 0.78 0.82 0.78 0.78 ... -

0.70 0.72 0.78 0.82 0.78 0.78 ... 1.00 -

Table 10.3: Section of the Pearson coefficient matrix for an average of matrices from 10 ms to 300 ms

GSAT_E1_F_B_m0400

GSAT_E1_F_B_m0500

0.82 0.95 -

0.70 0.85 0.90 -

0.59 0.76 0.82 0.91 -

0.59 0.75 0.81 0.88 1.00 -

... ... ... ... ... ... -

GSAT_E11_SW_T_p0400

GSAT_E1_F_B_m0300

0.87 -

GSAT_E11_SW_T_p0200

GSAT_E1_F_B_m0200

-

...

GSAT_E1_F_B_m0100

GSAT_E1_F_B_00000 GSAT_E1_F_B_m0100 GSAT_E1_F_B_m0200 GSAT_E1_F_B_m0300 GSAT_E1_F_B_m0400 GSAT_E1_F_B_m0500 ... GSAT_E11_SW_T_p0200 GSAT_E11_SW_T_p0400

GSAT_E1_F_B_00000

Pearson Coefficient

0.68 0.67 0.58 0.57 0.62 0.65 ... -

0.67 0.67 0.58 0.58 0.63 0.66 ... 1.00 -

10.2 Reduction of the Sensor Pool by Strongly Correlating Sensors

163

Figure 10.1: Overview of the sensor pool after reduction by correlation analysis (92 sensors)

can therefore be removed from the data pool. As was expected, the sensors behave symmetrically, i.e. sensor GSAT_E1_F_B_p0200 can also be removed from the data pool. If this procedure is applied to the entire matrix, then the original 219 sensors can be reduced to a remaining sensor set of 92 sensors. The original 11 sensor planes are reduced to the 6 front planes. The sensors in planes 7 to 11 fully correlate with sensors in the front 6 planes. Figure 10.1 shows the remaining sensors. If the methodology presented in Section 9.3 is applied to the 92 sensors that remain after the correlation analysis, then the result shown in Table 10.4 is generated. The table shows the MCITn values at the times considered. As was

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Chapter 10 Second Degree of Freedom: Selection of Sensors

to be expected, the behaviour is similar to that of the original 219 sensors. In order to be able to use the data pool for an application, further optimization is required. Table 10.4: MCITn values for all load cases with 92 sensors MCITn / [ms]

LC 10

13

16

17

20

28

32

35

39

41

51

56W 50W 40W 32W 27W 64Ol 64Or

7.1 6.0 4.0 2.4 1.9 2.6 2.6

10.4 7.6 4.8 3.4 4.0 4.0 1.3

17.5 15.6 11.8 8.1 5.8 5.8 5.8

19.4 17.5 13.3 9.4 6.7 6.4 6.5

25.4 23.3 18.6 13.6 10.1 8.5 8.5

41.4 39.2 33.5 27.4 21.2 14.9 15.0

49.4 47.2 41.2 34.6 27.9 18.6 18.8

55.4 53.2 46.9 40.1 33.0 21.5 21.7

63.4 61.2 54.5 47.6 40.0 25.6 25.7

67.4 65.2 58.3 51.3 43.5 28.0 27.9

87.4 85.2 77.8 70.6 62.0 42.3 41.8

40Wl 40Wr 56Or 56Or

0.6 0.6 2.1 2.1

1.3 3.3 3.3 0.7

2.4 2.4 4.6 4.7

2.9 2.9 5.1 5.1

4.8 4.8 6.8 6.8

12.3 12.3 12.0 11.8

16.1 16.1 16.2 16.1

19.1 19.1 16.5 16.5

25.0 25.0 21.0 21.2

27.5 27.5 22.7 23.1

41.9 41.9 33.8 34.5

32Wl 32Wr 40Ol 40Or 35P

0.3 0.3 1.4 1.3 0.6

0.7 2.2 2.2 1.2 0.1

1.3 1.3 3.1 3.1 2.1

1.6 1.5 3.5 3.5 2.5

2.5 2.4 4.4 4.4 3.5

6.5 6.0 7.4 7.4 7.2

9.0 8.7 9.2 9.2 9.8

11.1 11.0 10.6 10.6 11.8

14.1 14.4 14.5 13.6 15.0

14.7 15.2 14.7 14.8 16.7

24.6 25.4 20.5 20.9 26.3

30T

0.0

1.5

0.6

0.9

1.8

5.3

7.6

9.5

12.0

13.3

17.8

NM

0.8 0.6 0.5 1.0 0.7 0.7 0.9 0.7 0.6 0.0 0.0 0.0 0.0 0.0 0.0 0.0

1.1 0.8 1.7 1.2 1.0 1.7 1.2 0.9 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0

2.3 1.6 1.0 2.5 1.7 1.3 2.5 1.7 1.2 0.0 0.0 0.0 0.0 0.0 0.0 0.0

2.5 1.7 1.1 2.8 1.9 1.4 2.8 1.9 1.3 0.0 0.0 0.0 0.0 0.0 0.0 0.0

3.3 2.3 1.3 3.6 2.5 1.8 3.7 2.4 1.5 0.0 0.0 0.0 0.0 0.0 0.0 0.0

5.5 3.6 1.9 5.7 4.1 2.8 6.0 4.0 2.3 0.2 0.3 0.4 0.2 0.0 0.0 0.0

6.5 4.3 2.2 6.8 4.9 3.3 7.2 4.8 2.7 0.5 0.6 0.6 0.3 0.0 0.0 0.0

7.4 4.8 2.4 7.6 5.5 3.6 8.1 5.3 3.0 0.7 0.8 0.8 0.5 0.0 0.0 0.0

8.4 5.5 2.7 8.7 6.2 4.1 9.3 6.1 3.4 1.0 1.1 1.1 0.7 0.0 0.0 0.0

9.0 5.8 2.8 9.2 6.6 4.4 9.8 6.5 3.6 1.1 1.3 1.2 0.8 0.0 0.0 0.0

11.6 7.5 3.5 11.9 8.6 5.6 12.8 8.5 4.6 1.8 2.0 2.0 1.4 0.0 0.0 0.0

10.3 Methodology for Optimising the Sensor Data Pool In Section 9.4 it could be seen that, in many cases, the MCITn values displayed the desired behaviour over the different times considered, but that there is a

10.3 Methodology for Optimising the Sensor Data Pool

165

need for further optimisation in order to establish the correlation between the firing time requirements and the respective MCITn values. In order to optimize the sensor pool, it is therefore necessary to identify the sensors that have a negative influence on the result. If Table 10.4 is considered, then it can be seen that both at 32 ms and at 39 ms, the MCITn values of the ODB load cases are too great compared to those of the Wall 30° load cases. In the following, a method is therefore presented to determine the sensors that contribute significantly to this undesired behaviour. In order to evaluate the sensor results with regard to the present question of an overrepresentation of the MCITn values for certain load cases, it is necessary to determine these critical sensors. For this purpose, a data cloud is formed for each sensor at the critical times (32 ms and 39 ms) from the firing time requirements of all load cases and their respective CI values. A linear regression line can then be determined for this point cloud, with the help of which the contribution of the respective sensor to the unwanted behavior can be analyzed. [Cleff, 2015] Regression analysis attempts to place a straight line representing the relationship between the data points in the scatter plot of the data cloud. In the present case, these are two values that represent an expected linear relationship. This regression line can be represented by the following functional equation. ˆyi = b0 + b1 · x i

(10.3)

With: ˆyi b0 b1

estimation of the CIn value for the corresponding firing time x of the i-th load case intersection with the y-axis gradient of the straight line

The determining variables for the regression line, the regression coefficients, are the values b0 and b1 which unambiguously describe the position of the straight line. In an ideal regression line, as shown in Diagram 10.1, the estimated and measured values are exactly on top of each other on the line. To determine the

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Chapter 10 Second Degree of Freedom: Selection of Sensors

Diagram 10.1: Nearly ideal regression line with R2 ∼ 1

straight line, an attempt is made to place it in the scatter plot in such a way that the error rate in the estimate is as low as possible. Diagram 10.2 shows the formation of the residual for the point under consideration. It designates the distance between the predicted value ˆy and the true measured value y. To determine the regression coefficients, the methodology of ordinary least squares is applied. An attempt is made to define b0 and b1 in such a way that the sum of the squared residuals is minimal. This is the case if slope b1 is calculated as follows. b1 =

cov(x, y) s2x

With: cov(x,y) s2x

covariance of the two variables firing time requirement and crash intensity value variance of variables x

(10.4)

10.3 Methodology for Optimising the Sensor Data Pool

167

Diagram 10.2: Formation of the residual

After calculating b1 , b0 can be calculated from the equation and a pair of values. The slope b1 of the straight line can be regarded as a measure of how strongly the change in the firing time affects the magnitude of the CIn value for the respective sensor. The value is therefore referred to as the regression weight. In this application, b1 should always be less than 0, i.e. a higher firing time leads to a lower crash intensity. The steeper the straight line, the more pronounced the correlation. The coefficient of determination R2 his used to evaluate the quality of the regression line. The coefficient of determination relates the variance in the predicted values to the observed variance.

2

R =

s2ˆy s2y

(10.5)

The value of R2 varies between 0 and 1, where 1 gives an ideal correlation between estimated and measured values. [Urban and Mayerl, 2018; Fahrmeir et al., 2016]

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Chapter 10 Second Degree of Freedom: Selection of Sensors

Diagram 10.3: Regression analysis for the selection of sensors

The regression lines can be used to determine the sensors that best contribute to improving the ideal correlation between firing time requirements and CITn. If individual load cases are to be weakened or strengthened, it is necessary to identify those sensors which are located as perfectly as possible on the regression line for all other load cases and for which the value for the load case to be weakened or strengthened is as far away from the regression line as possible. Otherwise, if sensors with CI values that are all also far from the regression line are removed from the sensor pool, then this leads to the following problem: When these sensors are removed, the correlations of all other load cases to their firing times are altered and other critical load cases may be added. If, for example, all sensors are exactly on the regression line, then the sensor could be excluded from the analysis without significantly impairing the overall correlation. The more and the stronger the sensors deviate from the straight line, the more difficult it is to estimate the influence on the other crash intensity value correlations. To identify the sensors, the coefficient of determination R2 is determined first. The closer this value is to 1, the more non-reactively the sensor can be removed

10.4 Application of the Methodology

169

from the sensor pool. In descending order, sensors are searched where the load case to be changed is as far away as possible from the regression line and where all other values are as close as possible to the regression line. Diagram 10.3 shows an ideal example of this. All information measures, with the exception of the critical one, are on the straight line. Removing this sensor has a major effect on the desired solution. After removing the most suitable sensor, the influence of this action must be evaluated for all times considered. If the desired result has not yet been achieved, then the second-best sensor is considered. The process is repeated until the desired result is achieved. If there are non-correlating correlations for more than one critical firing time, an iterative approach is taken by removing or adding only one sensor for each critical firing time, evaluating the effects and then removing only one more sensor per firing time. This means that the procedure is iterated until the desired result, a correlation between all firing times and crash intensities, is achieved.

10.4 Application of the Methodology As shown in Section 9.4, two firing times display a non-correlating relationship between crash intensity values and firing time. At 32 ms and at 39 ms, a clear predominance of the ODB load cases compared to the Wall 30° load cases and the pole load case can be seen. If the sensors are evaluated according to the above methodology, then a set of 92 point clouds and regression lines per critical firing time is generated that can be evaluated (Diagram 10.4). Diagram 10.5 shows the sensor with the highest coefficient of determination R2 of 0.82. If the course of the information measures is considered over time, then there is no contribution to the solution of the problem at hand. If the sensor set is gone through in descending order of the coefficient of determination, then e.g. the sensor GSAT_E3_KPV_B_p0250 and its mirrored sensor GSAT_E3_KPV_B_m0250 in the third sensor plane offer a high potential for weakening the 35 km/h Pole load case, as shown in Diagram 10.6. If the methodology is applied until the two problem cases are solved, then it becomes apparent that a reduction to 71 sensors (Figure 10.2) achieves a result that meets expectations.

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Chapter 10 Second Degree of Freedom: Selection of Sensors

Diagram 10.4: Regression analysis of 92 sensors at 39 ms

Diagram 10.5: Sensor with the highest coefficient of determination at 39 ms (no potential for weakening the 35 km/h Pole load case)

10.4 Application of the Methodology

171

Table 10.5: MCITn values for all load cases with 71 sensors MCITn / [ms]

LC 10

13

16

17

20

28

32

35

39

41

51

56W 50W 40W 32W 27W 64Ol 64Or 40Wl 40Wr 56Or 56Or 32Wl 32Wr 40Ol 40Or 35P 30T

7.0 6.0 4.1 2.7 2.2 3.0 3.0 0.6 0.6 2.4 2.4 0.4 0.4 1.4 1.4 0.7 0.0

11.8 10.3 7.5 4.7 3.6 4.5 4.5 1.3 1.3 3.7 3.8 0.8 0.8 2.3 2.3 1.3 0.0

17.5 15.6 11.6 7.9 5.7 6.3 6.3 2.5 2.5 5.1 5.2 1.4 1.4 3.3 3.3 2.1 0.0

19.5 17.5 13.2 9.1 6.5 6.9 6.9 3.1 3.1 5.6 5.7 1.7 1.7 3.7 3.6 2.4 0.1

25.4 23.3 18.5 13.2 9.7 9.1 9.0 5.2 5.2 7.4 7.4 2.6 2.8 4.7 4.7 3.2 0.6

41.4 39.3 33.8 27.1 20.6 15.6 15.1 13.1 13.1 12.7 12.6 7.0 6.9 7.6 7.6 6.4 3.1

49.4 47.3 41.5 34.4 27.3 19.5 18.8 17.5 17.5 15.9 15.6 9.7 9.7 9.3 9.4 8.8 5.0

55.4 53.3 47.4 40.0 32.5 22.3 21.6 20.9 20.9 18.5 18.1 12.0 12.0 10.7 10.8 10.7 6.5

63.4 61.3 55.1 47.6 39.4 26.4 25.4 25.8 25.8 22.0 21.5 15.2 15.5 12.6 12.7 13.6 8.6

67.4 65.3 59.0 51.5 43.0 28.8 27.6 28.4 28.4 23.7 23.4 16.9 17.3 13.8 13.9 15.2 9.7

87.4 85.3 79.0 70.9 61.5 43.0 41.1 42.1 42.1 34.7 34.4 26.3 26.3 20.5 20.9 24.3 16.4

NM

0.9 0.7 0.6 1.1 0.8 0.8 1.0 0.8 0.7 0.0 0.0 0.0 0.0 0.0 0.0 0.0

1.6 1.2 0.9 2.0 1.3 1.1 1.8 1.4 1.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0

2.4 1.8 1.1 2.9 2.0 1.5 2.7 2.0 1.3 0.0 0.0 0.0 0.0 0.0 0.0 0.0

2.7 2.0 1.2 3.2 2.2 1.6 3.0 2.2 1.5 0.0 0.0 0.0 0.0 0.0 0.0 0.0

3.5 2.6 1.5 4.1 2.9 2.1 3.9 2.8 1.8 0.0 0.0 0.1 0.0 0.0 0.0 0.0

5.8 4.2 2.2 6.6 4.7 3.2 6.4 4.6 2.7 0.3 0.4 0.4 0.3 0.0 0.0 0.0

6.9 5.0 2.5 7.8 5.6 3.8 7.6 5.5 3.1 0.5 0.7 0.7 0.4 0.0 0.0 0.0

7.7 5.6 2.7 8.8 6.3 4.2 8.6 6.2 3.5 0.8 0.9 0.9 0.5 0.0 0.0 0.0

8.9 6.4 3.1 10.0 7.2 4.7 9.8 7.1 3.9 1.1 1.3 1.3 0.8 0.0 0.0 0.0

9.4 6.7 3.3 10.6 7.6 5.0 10.4 7.5 4.2 1.3 1.5 1.4 0.9 0.0 0.0 0.0

12.2 8.7 4.1 13.7 9.9 6.4 13.5 9.8 5.3 2.1 2.3 2.3 1.6 0.0 0.0 0.0

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Chapter 10 Second Degree of Freedom: Selection of Sensors

Diagram 10.6: Sensor with high potential for the 35 km/h Pole case at 39 ms

If the correlation of the crash intensity measures of the individual load cases to their respective firing times is considered, then the requirements are seen to be identical. Based on the sensor set with 71 sensors, a single-stage algorithm can be created (Table 10.5). An optimization to less than 71 sensors does not lead to the desired result, since the very dominant load cases 35 km/h Pole and 30 km/h Truck always display disproportionately high crash intensity values compared to the required firing times. Further reductions are only possible if the target of a single-stage algorithm is abandoned and if a multi-stage algorithm, for example a two-stage algorithm, can be used.

10.5 Summary In the previous chapter it was shown how the sensor spectrum can be reduced to 71 sensors and how, at the same time, the desired relationship between MCITn values and firing time requirements can be optimized. In Chapter 9, a methodology was presented to evaluate the data quality of the sensor measurements with regard to their ability to separate the various load cases. The

10.5 Summary

173

Figure 10.2: Overview of the sensor pool after reduction to 71 sensors

evaluation for the new sensor set is shown below to demonstrate that a similarly good separation capability can also be achieved with the reduced sensor set. If the evaluation of the Mojena criterion shown in Table 10.6 and the dendrogram shown in Diagram 10.7 are considered for all load cases using the reduced sensor set, then it can be seen that the quality is similar to that of the sensor set with 219 sensors. The presented methodology to reduce the sensor set did not lead to a deterioration of the data quality. The evaluation of the separation capability of the Fire load cases also shows a good result. In Table 10.7 the Mojena value decreases compared to the total sensor set of 219 sensors, but, at 1.3, it is still within a very good range. In the dendrogram shown in Diagram 10.8, a good separating ability between load cases with slow and with fast crash severity progress can still be detected. With the sensor set

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Table 10.6: Error square sums according to Ward and evaluation according to Mojena for 300 ms Fire and NoFire load cases with 71 sensors Number of clusters

Error sum of squares

Mojena criteria

10 9 8 7 6 5 4 3 2 1

4.17 4.18 4.59 6.13 6.16 6.25 8.14 8.32 9.88 41.66

-0.052 0.006 0.218 0.223 0.236 0.498 0.523 0.740 5.151

Diagram 10.7: Two clusters 300 ms Fire and NoFire load cases with 71 sensors

10.5 Summary

175

Table 10.7: Error square sums according to Ward and evaluation according to Mojena for 300 ms Fire load cases with 71 sensors Number of clusters

Error sum of squares

Mojena criteria

10 9 8 7 6 5 4 3 2 1

4.28 4.35 5.41 5.95 7.52 7.77 8.24 15.30 15.78 17.94

-0.262 -0.140 -0.079 0.102 0.131 0.185 0.997 1.052 1.300

Diagram 10.8: Two clusters 300 ms Fire load cases with 71 sensors

reduced to 71 sensors, input data is available for an algorithm on the basis of which timely firing for all load cases is possible.

11 Third Degree of Freedom: Application With the optimized sensor set determined in the previous chapters, a vehiclespecific algorithm can be developed on the basis of which the decision to fire the restraint systems is made. Diagram 11.1 shows the CITn values for all considered load cases in their temporal course and the behaviour of all considered load cases. The set of curves thus represents the number of load cases for which the algorithm must decide whether and, if so, when the restraint systems must be triggered. The aim of the application is to determine a threshold at which the firing time requirements for the respective load case are met with as few deviations as possible. In addition, the firing thresholds should show a linear and continuous behaviour in order to meet the requirements on a crash algorithm. In an ideal design, the applied threshold would cut all load cases without deviating from the requirements. In reality, however, this is not possible due to the requirements on linearity and consistency, so that an optimum compromise must be sought between all requirements. As described in Section 3.5, the time requirements result from the forward displacement of the occupant in the crash. The specification determined there describes a firing time specification in milliseconds. However, the actual goal of a restraint system adjustment, to which the correct firing time of the restraint systems makes a significant contribution, is the compliance with biomechanical limit values for the respective load case. In order to comply with these limit values, there are further control variables in addition to the correct firing time in order to influence this overall behaviour. The selection of belt tensioner force profiles or the tuning and geometry of the airbags are also important design criteria for meeting the requirements in the vehicle crash test. For this reason, deviations from the firing times determined from the 5-inch criterion are permitted to a limited extent. Deviations in the range of ±20% are tolerable in most vehicle applications, deviations of ±10% usually provide a very good result when considering the dummy loads in a crash. In order to evaluate the quality of the results and as a guideline for the application in this paper, a deviation of less than 20% from the firing time requirement is aimed for. © Springer Fachmedien Wiesbaden GmbH, part of Springer Nature 2020 A. Leschke, Algorithm Concept for Crash Detection in Passenger Cars, https://doi.org/10.1007/978-3-658-29392-5_11

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Diagram 11.1: CITn values for all load cases with 71 sensors

Within the scope of the work, the approaches listed below for finding an optimal threshold were examined and the results to be achieved were compared with each other. In the following, the methodology applied is described in detail using the solution with the best overall result as an example. Firing thresholds that were examined: 1. Simple straight line from the CITn values of the load case with the fastest firing time requirements (56 km/h Wall 0°) and of the load case with the slowest firing time requirements (30 km/h Truck) 2. Two straight lines formed from the respective load cases with the fastest and slowest firing time requirements. The first straight line is formed from the load cases with fast crash severity progress. The second straight line is formed from the load cases with slow crash severity progress. 3. Simple regression line from the CITn values of all load cases 4. Two regression lines, each formed from the CITn values of the load cases with fast and slow crash severity progress When using two straight lines, a transition area must be defined to avoid jumps. To do so, both straight lines are connected linearly.

11.1 Threshold Design Using a Double Regression Line as an Example

179

11.1 Threshold Design Using a Double Regression Line as an Example The aim of designing the application thresholds is to meet the firing time requirements of the respective load cases as precisely as possible. Since, however, the course of the CIT curves for the respective load case varies due to measurement and vehicle tolerances and since timely firing of the restraint systems is also to be ensured between the support points contained in the load case collective, jumps and non-linearities of the application thresholds must be avoided. Therefore, a threshold consisting of as few straight lines as possible is ideal. An increase in the crash intensity value in the individual load case due to the variations described must predictably lead to a corresponding reduction in the firing times. For this application, the use of two regression lines has proved to be the best compromise between a simple robust application with few parameters and the fulfilment of the firing time requirements. The first regression line is determined from the CITn values of the fast Wall 0° load cases. The CITn values of the individual load cases at the time of the required firing time are considered. The distribution and the resulting regression line which has been determined according to the procedure shown in Chapter 10 is shown in Diagram 11.2. If the regression line determined in this way is transferred to the illustration of the Wall 0° load cases as the application threshold, then this results in Diagram 11.3. The second regression line is determined from the CITn values of the load cases with slow crash severity progress (Diagram 11.4). If this regression line is transferred to the load case diagram as an application threshold, the diagram shown in Diagram 11.5 results. If both thresholds are applied to all load cases as shown in Diagram 11.6, then it can be seen that both thresholds are exceeded over time by the NoFire load cases. Since, however, unintentional firing of restraint systems in these NoFire scenarios must be safely avoided, a further application threshold is necessary to ensure this behaviour. If the third threshold (Diagram 11.7) is applied to ensure a differentiation from the NoFire load cases, then this threshold must be selected in such a way that it is at a balanced distance from the slow Fire load cases and from the fast NoFire

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Diagram 11.2: Regression of the Wall 0° load cases (fast crash severity progress)

Diagram 11.3: Application threshold of the Wall 0° load cases (fast crash severity progress )

11.1 Threshold Design Using a Double Regression Line as an Example

181

Diagram 11.4: Regression of load cases with slow crash severity progress

Diagram 11.5: Application threshold of load cases with slow crash severity progress

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Chapter 11 Third Degree of Freedom: Application

Diagram 11.6: Critical intersections of the application lines

load cases. In order to ensure robustness to signal variation within the load cases, it must be ensured that the thresholds maintain a minimum distance from the adjacent load curves over the entire period of time. A robust design should still show the desired behaviour with signal fluctuations in the CITn value of ±15%. If a differentiation from NoFire and Fire load cases results in a conflict, then the distance to the NoFire scenarios should be prioritized. Unintentional firing should always be avoided. Finally, all three thresholds are to be connected in such a way that a continuous threshold is created which represents the first regression line in the fast load case area and the second regression line in the slow load case area. Furthermore, this threshold must enable safe differentiation from the NoFire load cases in the time after the slowest load case. In particular, the transition area between the two regression lines must be designed in such a way that the corresponding application thresholds lead to triggering when the signal is increased or decreased. If the application shown in Diagram 11.8 is analysed with regard to its robustness against signal increases in the NoFire load case range, it can be seen that a very good behaviour with a large distance between the NoFire load cases and the application curve is present over almost the entire time range.

11.1 Threshold Design Using a Double Regression Line as an Example

183

Diagram 11.7: Representation of all three application thresholds for all load cases

Diagram 11.8: Representation of the total application threshold for all load cases

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Diagram 11.9: Robust delimitation of the application threshold against NoFire load cases

Diagram 11.9 shows the application threshold in relation to the NoFire scenarios. However, it can also be seen that in the range from 45 ms to 55 ms, the distance between the fast NoFire load cases no longer meets the requirements of a robust delimitation with a signal boost of +15%. For further improvement, further optimisation in terms of a local detailed progress analysis of the application threshold is required. Diagram 11.10 shows the application threshold, the fastest NoFire load case and the delta between the two curves. In order to further optimize the behaviour, it is expedient to switch from the previous procedure which was based on mathematical process conditions to an optimisation based on an expert estimate. An improvement of the NoFire robustness, however, can only be achieved with a deterioration of the firing performance, since every change to the second application line results in a shift away from the optimal course which had been obtained with the help of the regression analysis. If the firing time of a load case is worsened by NoFire optimisation, it is therefore necessary to check whether the resulting firing time is still within the design corridor of ±20% tolerance to the nominal firing time.

11.1 Threshold Design Using a Double Regression Line as an Example

185

Diagram 11.10: Optimisation potential of robustness

If a further application threshold is applied which is to provide the best balance between robustness against the fast NoFire load cases, compliance with the firing time requirements for the load cases in the section to be optimized and distance within the framework of the specifications on the scalability of load cases, then this results in the threshold shown in Diagram 11.11. With this additional threshold, the robustness against NoFire load cases can be improved significantly. Diagram 11.12 shows the final threshold together with all load cases. In summary, the methodology for the application of an optimal triggering threshold can be presented in five sub-steps as follows. 1. Regression analysis of the load cases with fast crash severity progress in order to determine the first application line 2. Regression analysis of the load cases with slow crash severity progress in order to determine the second application line 3. Application of a third application line to delimit against NoFire / Misuse load cases with a distance of ±15% to the slowest Fire load case and to the fastest NoFire load case

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Diagram 11.11: 4th threshold for optimization of robustness

Diagram 11.12: Final application threshold

11.1 Threshold Design Using a Double Regression Line as an Example

187

4. Application of the linear connecting lines between the application lines 1 to 3, taking into account the variation of the fastest and slowest load cases per group for a firing decision within the framework of the assigned threshold sections 5. Manual optimization of the threshold to achieve the best possible robustness against the NoFire load cases The same approach can be used to create the thresholds for the other three application variants, as described at the beginning of this chapter. Table 11.1 shows the result. The deviation from the firing time requirements in percent is considered in each case. Deviations in the range of ±15% from the requirement are shown in green, up to ±20% in yellow and ≥ ±20% in red. Table 11.1: Overall result for all application variants FTR [ms]

Deviation [%]

Firing Time [ms]

Deviation [%]

Firing Time [ms]

Deviation [%]

Firing Time [ms]

Deviation [%]

var. 4

Firing Time [ms]

var. 3

max.

var. 2

nom.

56W 50W 40W 32W 27W 64Ol 64Or 40Wl 40Wr 56Or 56Or 32Wl 32Wr 40Ol 40Or 35P 30T

var. 1

min.

LC

8 10 13 14 16 25 25 29 29 32 32 35 35 37 37 37 41

10 13 16 17 20 28 28 32 32 35 35 39 39 41 41 41 51

12 16 19 20 24 34 34 38 38 42 42 47 47 49 49 49 61

10 11 13 16.5 19.5 20 20.5 25 25.5 25 25 36.5 36 42 41.5 39 51

0.0 -15.4 -18.8 -2.9 -2.5 -28.6 -26.8 -21.9 -20.3 -28.6 -28.6 -6.4 -7.7 2.4 1.2 -4.9 0.0

10 11 13.5 17 20 27.5 28 29.5 30 31.5 32 39.5 39 44.5 44.5 42 51

0.0 -15.4 -15.6 0.0 0.0 -1.8 0.0 -7.8 -6.3 -10.0 -8.6 1.3 0.0 8.5 8.5 2.4 0.0

12 13 15.5 19 23 25 26 28 28.5 29.5 30 40.5 40 47 47.5 43 55

20.0 0.0 -3.1 11.8 15.0 -10.7 -7.1 -12.5 -10.9 -15.7 -14.3 3.8 2.6 14.6 15.9 4.9 7.8

11 12 14.5 18 21 29 29.5 30 30.5 32 32.5 40 39.5 44 43.5 41.5 53.5

10.0 -7.7 -9.4 5.9 5.0 3.6 5.4 -6.3 -4.7 -8.6 -7.1 2.6 1.3 7.3 6.1 1.2 4.9

Ø -12.3

Ø -2.6

Ø +1.3

Ø +0.6

With the exception of the first application variant, a simple min. / max. straight line, all considered variants lead to an acceptable result within the specified tolerances. Variant 4, described in detail, leads to the best result. All firing time

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requirements are fulfilled with a tolerance of max. ±10%. In addition, a very robust distance to the NoFire load cases is maintained.

11.2 Comparison of Application Results with the State-of-the-Art In Section 3.5, different algorithms that are based on total vehicle deceleration were presented. The two-stage algorithm concept described in [Kozyref, 2004] reflects the state-of-the-art and is used widely in the market. Based on this algorithm concept, an application was created for the vehicle model from the small car segment that has been used here. This application is used for comparison of the results provided by the one-stage CI algorithm. In order to be able to draw a comparison to another single-stage algorithm, the application result of a single-stage overall vehicle deceleration algorithm according to [Olsson, 1994] is presented. All applications required compliance with the robustness requirements against unintentional firing in NoFire and Misuse load cases and compliance with the firing time specifications over the entire load case set. The results are summarised in Table 11.2 Deviations of up to ±15% are indicated in green in the table, deviations between ±15% and ±20% in yellow and deviations beyond this in red. If the results are evaluated, then it becomes apparent that, as already described in Section 3.5, the single-stage algorithm based on the total vehicle deceleration according to [Olsson, 1994] no longer leads to satisfactory results when taking into account the current state of the art regarding requirement load cases. Almost all load cases clearly fail to meet the requirements. In order to achieve acceptable firing times over the entire load case set, triggering would have to be accepted in various misuse scenarios such as wildlife accidents or pothole passages. If the two-stage CVA according to the state-of-the-art as described in [Kozyref, 2004] is considered, then good to very good results are obtained for all but a few load cases. Only the fast ODB load cases at 56 km/h and 64 km/h do not fulfil the requirements. The same applies to the Truck load case. For these load cases, it must be decided within the framework of the assessment of the dummy load values whether further optimisation of the firing threshold is necessary. If a two-stage concept is maintained, then an improvement can be implemented, but leads to a deterioration of the NoFire / Misuse performance. If this procedure is not successful, then further sensors must be included in the

11.2 Comparison of Application Results with the State-of-the-Art

189

Table 11.2: Comparison of CIA to CVA based on [Olsson, 1994] and [Kozyref, 2004] Algorithm LC

FTR [ms]

one stage

nom.

max.

Firing Time [ms]

Deviation [%]

Firing Time [ms]

Deviation [%]

Firing Time [ms]

Deviation [%]

56W 50W 40W 32W 27W 64Ol 64Or 40Wl 40Wr 56Or 56Or 32Wl 32Wr 40Ol 40Or 35P 30T

CV algorithm

min.

CI algorithm

two stages

8 10 13 14 16 25 25 29 29 32 32 35 35 37 37 37 41

10 13 16 17 20 28 28 32 32 35 35 39 39 41 41 41 51

12 16 19 20 24 34 34 38 38 42 42 47 47 49 49 49 61

11 12 14.5 18 21 29 29.5 30 30.5 32 32.5 40 39.5 44 43.5 41.5 53.5

10.0 -7.7 -9.4 5.9 5.0 3.6 5.4 -6.3 -4.7 -8.6 -7.1 2.6 1.3 7.3 6.1 1.2 4.9

13 18 34 19 45 49 45 49 53 54 54 62 57 80 80 50 63

30.0 38.5 112.5 11.8 125.0 75.0 60.7 53.1 65.6 54.3 54.3 59.0 46.2 95.1 95.1 22.0 23.5

11 14 17 17 20 26 22 31 32 29 25 38 38 35 35 45 60

10.0 7.7 6.3 0.0 0.0 -7.1 -21.4 -3.1 0.0 -17.1 -28.6 -2.6 -2.6 -14.6 -14.6 9.8 17.6

Ø +0.6

Ø +60.1

Ø -3.6

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decision-making process. This makes the concept even more complex and more complex to apply. For ODB load cases, information from the y-sensor on the centre tunnel, which is used for side crash detection, is usually used. Due to the rotatory part in the crash course of the ODB load cases, additional information on crash discrimination can be determined in this way. The use of predictive sensors, as shown in Section 3.5 could be applied to the Truck load case. When evaluating the application results of the new CI algorithm, it is apparent that this algorithm performs significantly better. In all load cases from the design load case set, the deviations of the firing times from the specifications are considerably less than 10%. Only the fastest load case, the 56 km/h Wall 0° crash, has a deviation of exactly 10%, but this corresponds to only 1 ms due to the very fast firing time requirement. The approach developed in this paper shows clear advantages over the state-of-the-art. The newly introduced algorithm size CITn can fulfil all requirements in a one-stage approach. Further auxiliary variables are not necessary, i.e. the result is based on a continuous one-stage approach that has been generated physically and is reproducible.

11.3 Robustness of the Algorithm In the previous chapters, an algorithm concept based on simulation data was developed. It was shown that all requirements on a timely release of restraint systems in a crash can be safely fulfilled. In order to be able to evaluate the robustness of the concept against variations in the input signals of the algorithm and in the accident types, the following section specifically changes the input variables and evaluates the behaviour of the algorithm for accident types that are not included in the design load case set.

11.3.1 Variation of the Amplitude of the Measurement Signal In the course of the work so far, the algorithm was designed on the basis of nominal signals. In reality, the values measured in crash tests vary due to various influencing factors, such as production tolerances in the body-in-white or in the joining technology of the vehicles or due to component tolerances of the sensors or electronics used in the measurement technology chain. In order to include these variations in the application, this paper considers a

11.3 Robustness of the Algorithm

191

Figure 11.1: Amplitude variation of the raw sensor signals by ±15%

variation of ±15% in the signal amplitudes of the measurement signals of the individual acceleration sensors. This means that the raw sensor signals are increased and reduced by 15% as shown in Figure 11.1. Based on these new input signals, the corresponding CITn values are determined and evaluated using the CI algorithm. In accordance with the requirements on nominal signals, no deviations greater than ±20% from the firing time requirement should be permitted. The signal characteristics of the crash intensity measure are influenced by the change in the sensor signals. However, the signal change has a different effect for each sensor due to the underlying threshold evaluation. Four different categories can be distinguished: • Sensors which, for the load case under consideration, have not exceeded the 6 m/s threshold in the nominal signal range also do not exceed it after the signal increase. Their influence on the new overall signal curve does not change.

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Figure 11.2: Four categories of signal variation influence on CIT values

• Sensors which have not reached the 6 m/s threshold at the nominal signal exceed the threshold and therefore influence the result significantly. • Sensors which have exceeded the threshold at the nominal signal provide a greater contribution to the CITn value, but do not change the result as significantly as the sensors from the bullet point before. • If the raw signal is reduced, then these three groups are also found. The first time exceedance is supplemented by the category of first time undershooting the limit. Figure 11.2 shows the four categories. Table 11.3 shows the results of the applications with nominal signals and taking into account the signal variation of ±15%. In principle, the application on the basis of local component decelerations behaves very robustly in the event of an amplitude change. With the exception of a few load cases, the required firing times are achieved within the desired tolerance range. With a signal increase of +15%, an unwanted behaviour only becomes apparent in the case of one load case. As shown in Diagram 11.13,

11.3 Robustness of the Algorithm

193

Table 11.3: Firing times with an amplitude variation of ±15% FTR [ms]

Firing Time [ms]

Deviation [%]

Firing Time [ms]

Deviation [%]

Firing Time [ms]

Deviation [%]

+15 % signal

max.

nominal signal

nom.

56W 50W 40W 32W 27W 64Ol 64Or 40Wl 40Wr 56Or 56Or 32Wl 32Wr 40Ol 40Or 35P 30T

- 15 % signal

min.

LC

8 10 13 14 16 25 25 29 29 32 32 35 35 37 37 37 41

10 13 16 17 20 28 28 32 32 35 35 39 39 41 41 41 51

12 16 19 20 24 34 34 38 38 42 42 47 47 49 49 49 61

12 13 15.5 18.5 22 31.5 31.5 34 34 34 34.5 52.5 52.5 58 52.5 45 90

20.0 0.0 -3.1 8.8 10.0 12.5 12.5 6.3 6.3 -2.9 -1.4 34.6 34.6 41.5 28.0 9.8 76.5

11 12 14.5 18 21 29 29.5 30 30.5 32 32.5 40 39.5 44 43.5 41.5 53.5

10.0 -7.7 -9.4 5.9 5.0 3.6 5.4 -6.3 -4.7 -8.6 -7.1 2.6 1.3 7.3 6.1 1.2 4.9

11 11.5 14 17 19 21 21 29.5 30 29.5 29.5 35 35.5 39.5 37.5 40 45

10.0 -11.5 -12.5 0.0 -5.0 -25.0 -25.0 -7.8 -6.3 -15.7 -15.7 -10.3 -9.0 -3.7 -8.5 -2.4 -11.8

Ø +17.3

Ø +0.6

Ø -9.4

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Chapter 11 Third Degree of Freedom: Application

Diagram 11.13: Amplitude variation by +15%

the signal increase results in the 64 km/h ODB load cases being shifted to a temporal range in which they no longer intersect the second threshold section but lie within the trigger range of the first threshold section that is mapped by the Wall 0° regression. This results in a significantly increased firing time which is outside the desired tolerance range. If the result at a signal reduction of -15% is evaluated, then the picture shown in Diagram 11.14 is obtained. The reduction of amplitudes results in three areas of unwanted system behaviour. Triggering of the 56 km/h Wall 0° load case with its fastest firing time requirements in the total load collective is to too slow (1). The slow ODB and Wall 30° load cases are only triggered in the second transition area and therefore have an excessive firing time (2). The Truck load case is well below the threshold that has been optimized with regard to the distance to the NoFire load cases and, with a firing time of 90 ms, has a firing time that is clearly too long (3).

11.3 Robustness of the Algorithm

195

Diagram 11.14: Amplitude variation by -15%

11.3.2 Optimization of the Application Threshold In order to meet the requirements on a robust application, it makes sense to optimize the areas with unwanted system behaviour. When optimizing, it must be noted that moderate deteriorations regarding the firing times for nominal signals are acceptable, but that the nominal firing times remain at the forefront of the application. Since the problem cases depend in detail on the behaviour of the vehicle model, no general procedure can be defined for high-precision optimization. An optimization must be carried out on the basis of the expertise of the applicator. However, some general guidelines should be complied with: • The individual problem areas must be dealt with iteratively. After eliminating a problem area, the effect of the optimization on all load cases must be investigated. • The basic design of the threshold geometry should be adapted as little as possible so as not to lose the reference to the results of the regression analyses.

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Diagram 11.15: Threshold optimization

• The partial sections of the thresholds are only to be influenced with regard to the degrees of freedom "angle between the thresholds" and "length of the thresholds". If the application thresholds are optimized according to the problem areas and the methodology shown, then the procedure shown in Diagram 11.15 results. In order to optimise the firing times, five actions were performed, each of which addressed one or more of the identified problem areas of the amplitude variation. As a first step, the Wall 0° threshold was slightly lowered (1) and shortened (2). This improves the late firing times in this area and prevents the triggering of 64 km/h ODB load cases in the case of a variation of +15%. In the next step, a greater negative slope was imposed on the second trigger threshold, and the second transition threshold was extended at the same time. With this measure, the late firing times of the slow ODB and Wall 30° load cases are optimized and the firing time of the Truck load case is shortened significantly. As a final measure, a greater positive slope was imposed on the threshold in the third transition area. This further improves the distance to the NoFire and Misuse load cases.

11.3 Robustness of the Algorithm

197

Diagram 11.16: Total application with nominal signals and ±15% amplitude variations

If this newly generated threshold is applied to the representation of all load cases in a diagram with the CITn nominal values and the curves varied by ±15% then the following final overall picture of the application results (Diagram 11.16). Table 11.4 shows the resulting total firing times. The firing times resulting from the optimisation show a good compromise over the entire width of the amplitude variation. All firing times display a deviation of less than 20% from the requirements. The overall application shows a very good and robust performance and a clear differentiation from the NoFire and Misuse load cases.

11.3.3 Comparison of the Results with the State-of-the-Art The results of the signal variations were also to be compared with the state-of-theart. To do so, the firing time results for the ±15% signal variation of the vehicle deceleration were also calculated and optimized for the algorithm according to the state-of-the-art described in [Kozyref, 2004]. The result is shown in Table 11.5 in comparison to the CI algorithm. A comparison with the state of

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Table 11.4: Optimized firing times with an amplitude variation of ±15% FTR [ms]

Firing Time [ms]

Deviation [%]

Firing Time [ms]

Deviation [%]

Firing Time [ms]

Deviation [%]

+15 % signal

max.

nominal signal

nom.

56W 50W 40W 32W 27W 64Ol 64Or 40Wl 40Wr 56Or 56Or 32Wl 32Wr 40Ol 40Or 35P 30T

- 15 % signal

min.

LC

8 10 13 14 16 25 25 29 29 32 32 35 35 37 37 37 41

10 13 16 17 20 28 28 32 32 35 35 39 39 41 41 41 51

12 16 19 20 24 34 34 38 38 42 42 47 47 49 49 49 61

11.5 12.5 15 18 23 30.5 30.5 32.5 32.5 32.5 32.5 45.5 41.5 42 41 40 61

15.0 -3.8 -6.3 5.9 15.0 8.9 8.9 1.6 1.6 -7.1 -7.1 16.7 6.4 2.4 0.0 -2.4 19.6

10.5 11.5 14 17 22 28.5 29 29.5 30 30.5 30.5 36.5 36.5 38.5 38.5 38 47

5.0 -11.5 -12.5 0.0 10.0 1.8 3.6 -7.8 -6.3 -12.9 -12.9 -6.4 -6.4 -6.1 -6.1 -7.3 -7.8

10.5 11 13.5 16 18 26.5 27 28.5 29 29 29 33.5 34 35.5 34.5 37 41

5.0 -15.4 -15.6 -5.9 -10.0 -5.4 -3.6 -10.9 -9.4 -17.1 -17.1 -14.1 -12.8 -13.4 -15.9 -9.8 -19.6

Ø +4,7

Ø -4,9

Ø -11,2

11.3 Robustness of the Algorithm

199

Table 11.5: Comparison CIA to CVA for nominal and amplitude-varied signals LC

CI algorithm

Deviation [%]

Firing time [ms]

Deviation [%]

Firing time [ms]

Deviation [%]

Firing time [ms]

Deviation [%]

+15 % sig.

Firing time [ms]

nom. sig.

Deviation [%]

-15 % sig.

Firing time [ms]

+15 % sig.

Deviation [%]

56W 50W 40W 32W 27W 64Ol 64Or 40Wl 40Wr 56Or 56Or 32Wl 32Wr 40Ol 40Or 35P 30T

nom. sig.

Firing time [ms]

-15 % sig.

CV algorithm

11.5 12.5 15 18 23 30.5 30.5 32.5 32.5 32.5 32.5 45.5 41.5 42 41 40 61

15 -4 -6 6 15 9 9 2 2 -7 -7 17 6 2 0 -2 20

10.5 11.5 14 17 22 28.5 29 29.5 30 30.5 30.5 36.5 36.5 38.5 38.5 38 47

5 -12 -13 0 10 2 4 -8 -6 -13 -13 -6 -6 -6 -6 -7 -8

10.5 11 13.5 16 18 26.5 27 28.5 29 29 29 33.5 34 35.5 34.5 37 41

5 -15 -16 -6 -10 -5 -4 -11 -9 -17 -17 -14 -13 -13 -16 -10 -20

9 13 13 13 19 25 22 30 32 26 25 39 39 32 32 42 50

-10 0 -19 -24 -5 -11 -21 -6 0 -26 -29 0 0 -22 -22 2 -2

11 14 17 17 20 26 22 31 32 29 25 38 38 35 35 45 60

10 8 6 0 0 -7 -21 -3 0 -17 -29 -3 -3 -15 -15 10 18

11 14 17 17 24 27 26 33 41 33 35 41 41 52 52 46 60

10 8 6 0 20 -4 -7 3 28 -6 0 5 5 27 27 12 18

Ø +4

Ø -5

Ø -11

Ø -11

Ø -4

Ø +9

the art according to [Olsson, 1994] is omitted, as the latter could not provide a result that fulfilled the requirements when evaluating the nominal signals. In this comparison, the CI algorithm again confirms its better performance compared to the CV algorithm. Particularly in the ODB load cases, the CIA provides a much more robust result for both the +15% and the -15% signal variation. For the amplitude variation within the tolerance range, all deviations from the firing time requirements are less than ±20%. In the CV algorithm, these problem areas would have to be addressed by the addition of further sources of information. The CIA meets all requirements in a single-stage algorithm approach.

11.3.4 Variation of the Load Case Set Another approach to assessing the robustness of algorithms is to examine the behaviour regarding load cases that have not explicitly been considered in

200

Chapter 11 Third Degree of Freedom: Application

the design. In a robust algorithm concept, such load cases should come to a firing decision close to their time requirement. In the normal application process for new vehicles, such modified requirements would be included in the basic application. However, the methodology of evaluating the results of nonoptimized applications that is presented here can be used as a good indicator of robust behaviour. Three groups are defined and evaluated in the CIA for the analysis of the described robustness behaviour. 1. Group: New Fire load cases with changed barrier type • 90 km/h Oblique • 50 km/h MPDB 2. Group: Reduction of the speed of a known Fire load case so that it becomes a NoFire load case • 20 km/h ODB left (NoFire) 3. Group: Variation NoFire / Misuse load case • 16 km/h Wall 0° (light parking accident, rigid barrier) The two load cases from Group 1 that are evaluated here are shown in Figure 11.3. Both load cases differ significantly from the load cases in the design set. While only static barriers are used there, these new tests are carried out with moving barriers and are therefore even more similar to the behaviour of vehicle-to-vehicle collisions in accidents. Both requirements result from announcements by NCAP organizations and will supplement the load case set in the period after 2020 [carhs.training GmbH, 2017]. 11.3.5 Firing Times of the New Load Cases If the above four load cases are simulated including a signal variation of ±15% for the MPDB and Oblique load cases, and if they are included in the firing application of the CIA, then the behaviour shown in Diagram 11.17 is generated. The two NoFire load cases are well below the firing threshold and, as intended, they do not result in the triggering of any restraint systems. For nominal signals, the new Oblique load case exceeds the firing threshold at 13.5 ms. The required

11.4 Summary

201

Figure 11.3: New load cases (Oblique and MPDB load case)

firing time for this load case is 14 ms. For nominal signals, the MPDB load case exceeds the threshold at 11 ms. The required firing time here is 12 ms. This means that, in these crashes which are not included in the load case set, firing is very well within the requirements tolerance. The algorithm is very robust for all examples considered here. The results of the signal variation are also very good compared to the firing time requirements. The CI algorithm is very robust against the variation of load cases. If the Oblique load case is fed into the non-optimized CV algorithm, then firing is achieved at 17 ms when using nominal signals. The amplitude variation also deviates significantly from the firing time requirements. For the CV algorithm to meet the requirements on this new load case, a completely new application including the new load cases must be created. The CI algorithm is much more robust.

11.4 Summary In the previous chapter, a methodology was presented which can be used to determine an application threshold for the timely firing of restraint systems

202

Chapter 11 Third Degree of Freedom: Application

Diagram 11.17: Application of the three new load case groups, MPDB and Oblique nominal and ±15% amplitude variation

in a crash. The firing time results achieved were compared with the state-ofthe-art and with the requirements set. It could be shown that the CI algorithm meets all requirements. The robustness of the new algorithm concept was investigated using two different methodologies. The requirements could be fulfilled both when varying the amplitudes of the input signals and with regard to the behaviour in the case of accident types that were not included in the design load case set. Overall, the CIA shows a very robust behaviour. If the algorithm for the firing decision is classified according to the methodology presented, then the picture shown in Figure 11.4 is obtained. The algorithm is single-stage and can be implemented independent of time.

11.4 Summary

203

Table 11.6: Comparison of CIA to CVA for nominal and amplitude-varied signals FTR [ms]

Load case

CI algorithm

max.

Firing time [ms]

Deviation [%]

Firing time [ms]

Deviation [%]

Firing time [ms]

Deviation [%]

12.6 10.8 -

14 12 -

16.8 14.4 -

15.5 11.5 -

10.7 -4.2 -

13.5 11 -

-3.6 -8.3 -

13 10.5 -

-7.1 -12.5 -

Ø +3,3

FTR [ms]

Ø -6,0

Ø -9,6

CV algorithm

17

21.4

17

Ø +21,4

21.4

26

Ø +21,4

Figure 11.4: Model description of main algorithm for firing decision

Deviation [%]

Firing Time [ms]

16.8

+15% signal Firing Time [ms]

max.

14

Firing Time [ms]

nom.

12.6

nom. signal

Deviation [%]

min

-15% signal

Deviation [%]

Load case

90 km/h Oblique

+15% signal

nom.

90 km/h Oblique 50 KM/h MPDB 20 km/h ODB left 16 km/h Wall 0°

nom. signal

min.

-15% signal

85.7 Ø +85,7

12 Algorithm Concept for the Classification of Load Cases In the previous chapter, a methodology was presented to derive a single-stage algorithm from the CIT values, on the basis of which a timely firing of restraint devices can be performed. This chapter proves that the information content of the CI values is also sufficient to classify the Fire load cases according to crash type and direction. The following groups are considered: Crash type • • • • •

Wall 0° Wall 30° ODB Pole Truck

Hit direction • Left • Right In line with the requirements, single-stage threshold-based algorithms were to be developed for the classification.

12.1 Methodology for Determining the Sensors for Classification Within the framework of the main algorithm, on the basis of which the timely firing decisions for the various load cases are made, the CIT value was determined across all sensors of the respective sensor collective. In order to generate a classification algorithm, it is necessary to identify those sensors from the entire sensor set which have characteristic features for the respective group to be separated. In order to identify these sensors, a methodology is proposed © Springer Fachmedien Wiesbaden GmbH, part of Springer Nature 2020 A. Leschke, Algorithm Concept for Crash Detection in Passenger Cars, https://doi.org/10.1007/978-3-658-29392-5_12

206

Chapter 12 Algorithm Concept for the Classification of Load Cases

which uses a statistical method, discriminant analysis, to select the appropriate sensors. Discriminant analysis is a method from multivariate statistics. Essentially, it can be used to investigate one or more groups (crash types, hit directions) with regard to a large number of variables (sensor values). The result is a statement as to which sensors are most suitable for distinguishing between the groups considered. Multivariate statistics offers various methods for evaluating the quality of the separability. For this work the Wilks’ Lambda criterion is applied. Wilks’ Lambda is an "inverse" quality measure, i.e. smaller values mean higher separating force of the discriminant function and vice versa. No further explanation of the procedure is provided and reference is made to [Backhaus et al., 2016]. This procedure can be used to determine those sensors that are most relevant for the separability of the groups, e.g. different load cases among each other. Discriminant analysis does not provide any information as to whether the distinction between the individual characteristics (CI values of the sensors) results in a positive or a negative delta between the respective sensor values. In a threshold-based method, it is necessary that the group of load cases to be distinguished has a higher CITn value for the sensors used than all other groups. Otherwise, no classification threshold can be introduced into the application. A second selection criterion is therefore needed in order to take this decision. The amount of the positive delta between the slowest load case of the group to be demarcated and all other load cases is evaluated. The aim of the two analyses is to identify the sensors that are most suitable for separating a given piece of information. To do so, both conditions described must be fulfilled at the same time. Wilks’ Lambda as the quality criterion of the selectivity must be low, and there must be a high positive delta between the CI values of the two groups to be separated. Another criterion is a requirement on the temporal separation of the respective load case group. A classification of the respective load case as belonging to a group must be made before the time of the firing decision, otherwise this additional input variable cannot be used meaningfully in the crash algorithm. Since a classification must be made for all load cases within the respective group, the separation capability must be ensured over the entire relevant period of time

12.1 Methodology for Determining the Sensors for Classification

207

of the firing requirements of the group. A consideration is therefore made for different temporal interpolation points within the temporal firing time corridor of the load case groups. The methodology is explained below using an example. In the first step, as shown in Table 12.1, the Wilks’ Lambda values are determined for all sensors in the discriminant between the load case group under consideration and all other load cases. Table 12.1: Wilks’ Lambda values

Wilks’ Lambda

Sensor: GSAT_...

Wilks’ Lambda

Sensor: GSAT_...

Wilks’ Lambda

Group 1 - other LC (40 ms)

Sensor: GSAT_...

Group 1 - other LC (35 ms)

Ranking

Group 1 - other LC (30 ms)

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 ... 69 70 71

E3_KPV_T_00000 E2_QT_T_m0385 E2_F_T_m0400 E2_F_T_p0400 E2_QT_M_m0385 E2_QT_T_p0385 E2_F_T_m0500 E5_SWF_T_m0575 E3_KPV_T_p0250 E3_KPV_T_m0250 E2_QT_M_p0385 E1_F_B_p0100 E1_F_B_00000 E1_F_B_m0100 E2_F_T_p0500 E1_F_M_m0200 E1_F_B_p0300 E1_F_M_p0200 E5_SWF_T_p0575 E3_KPH_T_m0300 ... E5_RI_M_m0500 E5_RI_M_p0500 E6_SWF_T_p0685

0.78 0.80 0.80 0.88 0.88 0.88 0.92 0.93 0.94 0.94 0.95 0.96 0.96 0.97 0.97 0.97 0.97 0.97 0.97 0.98 ... 1.00 1.00 1.00

E3_KPV_T_00000 E2_F_T_m0400 E2_QT_M_m0385 E2_QT_T_m0385 E3_KPV_T_m0250 E2_F_T_p0400 E2_F_T_m0500 E3_KPH_T_m0300 E3_KPV_T_p0250 E2_QT_T_p0385 E5_SWF_T_m0575 E2_QT_M_p0385 E2_F_T_p0500 E3_KPH_T_p0300 E1_F_B_p0100 E1_F_B_00000 E5_SWF_T_p0575 E1_F_M_m0200 E1_F_M_p0200 E1_F_B_p0300 ... E5_RI_M_m0500 E5_RI_M_p0500 E6_SWF_T_p0685

0.79 0.80 0.81 0.82 0.86 0.88 0.88 0.89 0.89 0.89 0.91 0.94 0.94 0.95 0.95 0.96 0.96 0.97 0.97 0.97 ... 1.00 1.00 1.00

E2_QT_M_m0385 E2_F_T_m0400 E3_KPV_T_00000 E2_QT_T_m0385 E2_F_T_m0500 E3_KPV_T_m0250 E3_KPH_T_m0300 E2_F_T_p0400 E3_KPV_T_p0250 E5_SWF_T_m0575 E2_QT_T_p0385 E2_F_T_p0500 E2_QT_M_p0385 E3_KPH_T_p0300 E3_KPH_M_m0300 E1_F_B_p0100 E1_F_B_00000 E1_F_B_m0100 E3_KPH_M_p0300 E1_F_M_m0200 ... E5_RI_M_m0500 E5_RI_M_p0500 E6_SWF_T_p0685

0.79 0.81 0.83 0.84 0.87 0.87 0.87 0.88 0.89 0.90 0.90 0.93 0.93 0.93 0.95 0.95 0.96 0.96 0.96 0.97 ... 1.00 1.00 1.00

Since the load case group 1 shown here has firing times between 30 ms and 40 ms, the values are calculated and arranged for the three support points 30 ms, 35 ms and 40 ms. The sensors with the lowest values offer the best separation capability compared to the other groups. The selection of the suitable sensors is made, starting with the sensor with the lowest Wilks’ Lambda, via all three temporal

208

Chapter 12 Algorithm Concept for the Classification of Load Cases

support points. It should be noted that, due to the symmetry requirements on the crash algorithm, when selecting a sensor on one side, its counterpart on the opposite side is always included in the classification set regardless of its Wilks’ Lambda value. Table 12.1 shows seven sensors as having the best separation capability. If the CI values of these sensors are considered over the three temporal support points and if the difference between the slowest load case of this group 1 and all other load cases is calculated, then the picture shown in Table 12.2 results. Table 12.2: Delta of CIT values

Loadcase 3

Loadcase 4

Loadcase 5

Loadcase 1

Loadcase 2

Loadcase 3

Loadcase 4

Loadcase 5

40 ms

Loadcase 2

30 ms Loadcase 1

Group 1 - other LC

GSAT_E2_F_T_m0400 GSAT_E2_F_T_p0400 GSAT_E2_QT_M_m0385 GSAT_E2_QT_M_p0385 GSAT_E2_QT_T_m0385 GSAT_E2_QT_T_p0385 GSAT_E3_KPV_T_00000

2 3 11 9 22 25 -2

-8 -7 11 12 22 6 7

-3 -1 11 -2 22 2 4

-6 1 11 9 6 25 12

-12 -16 11 5 22 7 11

-1 -2 37 13 39 37 -4

-12 -10 37 17 39 37 5

-4 -1 11 8 22 -1 7

-4 0 11 9 6 21 12

-14 -12 11 5 20 5 12

Total

70

43

33

58

28

119

113

42

55

27

The two sensors GSAT_E2_F_T_m0400 and GSAT_E2_F_T_p0400 offer a high Wilks’ Lambda value, but most Delta CI values are smaler than those of the other load cases. With regard to threshold-based triggering, these sensors therefore discriminate in the wrong direction and must therefore be removed from the classification set of sensors. Accordingly, the remaining five sensors are used for discrimination.

12.2 Classification of Wall 0° Load Cases In the first step, the separability of the Wall 0° load cases from the rest of the load cases is investigated. For this purpose, a discriminant analysis was carried out, and the respective Wilks’ Lambda was determined for all sensors. Subsequently, the Delta CI values were determined. Depending on the type of classification,

12.2 Classification of Wall 0° Load Cases

209

separability must be before the corresponding firing time requirements. Wall 0° load cases therefore require separation between 10 ms and 20 ms. In order to obtain a robust design for the classification algorithms, the CI values of the nominal signals and of the signals varied by ±15% are considered. Due to the firing time requirements of the Wall 0° load cases, the time points at 10 ms and 20 ms were analysed. In order to obtain the most suitable sensors for the classification of the Wall 0° load cases, the load cases from the total collective were divided into two load case classes. This generates the representation described in Table 12.3, which can be used for further analysis. Table 12.3: Division into load case groups for Wall 0° classification Load case

Group

56 km/h Wall 0° 50 km/h Wall 0° 40 km/h Wall 0° 32 km/h Wall 0° 27 km/h Wall 0°

1 1 1 1 1

64 km/h ODB left 64 km/h ODB right 40 km/h Wall 30° left 40 km/h Wall 30° right 56 km/h ODB left 56 km/h ODB right 32 km/h Wall 30° left 32 km/h Wall 30° right 40 km/h ODB right 35 km/h Pole ... 10 km/h RCAR Bumper center 10 km/h RCAR Bumper right

2 2 2 2 2 2 2 2 2 2 2 2 2

The two load case groups shown in Table 12.3 were subjected to a discriminant analysis, and the corresponding Wilks’ Lambda values were determined. The result is shown in Table 12.4. The sensors were sorted according to their Wilks’ Lambda values. In contrast to the findings on load case groups with later firing time requirements that are described in the following, the Wilks’Lambda of the sensors scatter strongly. This is mainly due to the fact that, at the 10 ms time interval, many sensors have not yet triggered.

210

Chapter 12 Algorithm Concept for the Classification of Load Cases

Diagram 12.1: Application Wall 0° classification for nominal signals

Due to the symmetry requirements, the corresponding mirrored sensors were supplemented, if necessary. At 20 ms, a very good Wilks’ Lambda value of less than 0.3 is obtained for the sensors considered. At 10 ms, the Wilks’ Lambda values increase significantly for all sensors considered, but the selected sensors are still included in the group with the highest Wilks’ Lambda values. In the second step, the Delta CI values of the two load case groups were compared with each other. Table 12.5 shows the results for the selected sensors in comparison of the lowest value of the load case group Wall 0° with the greatest value of the other load case group. Since all sensors display the same tendency over the two temporal support points, the results are presented as a summary. For the selected sensors, all Delta CI values meet the requirements on a positive discriminant against the other load cases over all time intervals considered. A threshold-based application for classification can be created on the basis of these sensors. Diagram 12.1 shows the result for all load cases. The CITn values of the load cases in their temporal course are considered in each case. The load cases of the Wall 0° group are shown in blue, all other load cases are shown in green.

12.2 Classification of Wall 0° Load Cases

211

Table 12.4: Wilks’ Lambda values for Wall 0°

Sensor

Wilks’ Lambda

Sensor

Wilks’ Lambda

Wall 0° - other LC (20ms)

Ranking

Wall 0° - other LC (10ms)

1 2 3 4 5 6 7 8 9 10 11 12 13 14 ... 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 ... 70 71

GSAT_E1_F_B_p0100 GSAT_E2_QT_M_m0300 GSAT_E2_QT_M_p0300 GSAT_E1_F_M_00000 GSAT_E1_F_B_m0100 GSAT_E1_F_B_m0300 GSAT_E1_F_B_p0300 GSAT_E2_QT_M_m0400 GSAT_E2_QT_T_p0385 GSAT_E2_QT_M_m0100 GSAT_E2_QT_M_p0100 GSAT_E2_QT_M_p0400 GSAT_E2_QT_M_m0385 GSAT_E2_DFE1_M_m0485 ... GSAT_E2_DFE_M_p0450 GSAT_E3_KPV_T_00000 GSAT_E3_KPV_T_m0250 GSAT_E5_SWF_T_m0575 GSAT_E2_F_B_m0700 GSAT_E5_SWF_T_p0575 GSAT_E2_QT1_M_m0450 GSAT_E2_QT_B_m0200 GSAT_E2_QT_B_p0200 GSAT_E2_F_B_p0600 GSAT_E1_F_M_m0500 GSAT_E2_QT_B_00000 GSAT_E1_F_M_p0500 GSAT_E2_F_B_p0700 GSAT_E2_DFE2_M_m0485 GSAT_E2_DFE2_M_p0485 ... GSAT_E6_SWF_T_m0685 GSAT_E6_SWF_T_p0685

0.42 0.42 0.42 0.42 0.43 0.48 0.48 0.56 0.58 0.58 0.58 0.59 0.64 0.64 ... 0.83 0.83 0.83 0.83 0.83 0.86 0.86 0.86 0.86 0.88 0.88 0.89 0.91 0.92 ... -

GSAT_E2_QT_M_m0300 GSAT_E2_QT_M_p0300 GSAT_E2_QT_M_p0400 GSAT_E2_DFE1_M_m0485 GSAT_E2_QT_M_m0400 GSAT_E3_KPV_T_00000 GSAT_E2_QT_T_m0385 GSAT_E2_DFE_M_m0450 GSAT_E2_DFE_M_p0450 GSAT_E2_QT_M_m0100 GSAT_E2_QT_M_p0100 GSAT_E2_QT_M_m0385 GSAT_E2_QT_T_p0385 GSAT_E2_F_T_m0400 ... GSAT_E4_F_T_p0700 GSAT_E1_F_B_m0300 GSAT_E3_DFE_M_m0485 GSAT_E3_KPH_M_p0300 GSAT_E3_KPH_M_m0300 GSAT_E1_F_B_m0100 GSAT_E2_F_B_m0700 GSAT_E2_QT_M_p0385 GSAT_E1_F_B_p0100 GSAT_E1_F_B_p0300 GSAT_E6_SWF_T_p0685 GSAT_E1_F_B_m0500 GSAT_E2_QT1_M_m0450 GSAT_E2_F_B_m0600 GSAT_E4_F_T_m0700 GSAT_E3_F_T_p0600 ... GSAT_E2_QT_B_p0200 GSAT_E3_KPH_B_p0300

0.07 0.08 0.14 0.15 0.19 0.25 0.26 0.26 0.31 0.34 0.35 0.36 0.37 0.37 ... 0.57 0.58 0.59 0.60 0.60 0.61 0.62 0.63 0.63 0.64 0.64 0.64 0.65 0.66 0.66 0.67 ... 0.83 0.84

212

Chapter 12 Algorithm Concept for the Classification of Load Cases

Table 12.5: Analysis of the Delta CI values for the Wall 0° classification

10 km/h RCAR Bumper right

...

40 km/h ODB left

32 km/h Wall 30° right

32 km/h Wall 30° left

56 km/h ODB right

56 km/h ODB left

40 km/h Wall 30° right

40 km/h Wall 30° left

64 km/h ODB right

64 km/h ODB left

Wall 0° - other LC [10ms, 20ms]

GSAT_E1_F_B_p0100 GSAT_E1_F_B_m0100 GSAT_E2_QT_M_m0300 GSAT_E2_QT_M_p0300 GSAT_E2_QT_M_p0400 GSAT_E2_QT_M_m0400 GSAT_E3_KPV_T_00000

It is clearly visible that, with the selected threshold, all load cases of the Wall 0° group are above the threshold and all other load cases below the threshold. The load cases can be clearly separated from each other; thus, a classification is possible for the Wall 0° load cases. If the load cases for which the amplitude has been varied by ±15% are added, then the result is as shown in Diagram 12.2. The amplitude-varied load cases also show a clear separation of the two groups. Thus, the classification of the Wall 0° load cases proves to be very robust against signal scattering. The time of classification can be determined on the basis of the application. The result is shown in Table 12.6. Table 12.6: Classification times of the Wall 0° load cases Classification time [ms]

Load case

56 km/h Wall 0° 50 km/h Wall 0° 40 km/h Wall 0° 32 km/h Wall 0° 27 km/h Wall 0°

-15% signal

nom. Signal

+15% signal

3.5 3.5 3.5 3.5 4

3 3 3.5 3.5 3.5

3 3 3 3.5 3.5

12.2 Classification of Wall 0° Load Cases

213

Diagram 12.2: Application Wall 0° classification for nominal and ±15% signals

The Wall 0° load cases can be clearly classified between 3 ms and 4 ms. Since the firing time requirements are between 10 ms and 20 ms, timely classification is possible. If the classification algorithm is evaluated according to the methodology described in Chapter 5, then the result is as shown in Figure 12.1. The algorithm can be executed as a single stage and independently of time. If the Wall 0° load cases with their Delta CI values are analysed in detail in relation to all other load cases, it is evident that virtually all Wall 0° load cases discriminate against in a positive direction, irrespective of their Wilks’ Lambda values. Table 12.7 shows this behaviour as an example for the 20 ms time period. The Delta CI values, which make a positive contribution to the discrimination of the Wall 0° load cases, are shown in green. The Delta CI values, which provide a positive value for other load cases, are shown in red. Apart from a few exceptions, for example the sensor GSAT_E1_F_M_m0500, the influence of the Wall 0° load cases is clearly greater. A discriminant analysis of further time grid points shows similar results. Separation of the other load case groups from the Wall 0° load

214

Chapter 12 Algorithm Concept for the Classification of Load Cases

Table 12.7: Analysis of the Delta CI values of the Wall 0° load cases in relation to all other load cases

64 km/h ODB right

40 km/h wall 30° left

40 km/h wall 30° right

56 km/h ODB left

56 km/h ODB right

32 km/h wall 30° left

...

80 km/h animal left

70 km/h animal left

60 km/h animal left

...

10 km/h RCAR right

GSAT_E1_F_B_00000 GSAT_E1_F_B_m0100 GSAT_E1_F_B_m0300 GSAT_E1_F_B_m0500 GSAT_E1_F_B_p0100 GSAT_E1_F_B_p0300 GSAT_E1_F_B_p0500 GSAT_E1_F_M_00000 GSAT_E1_F_M_m0200 GSAT_E1_F_M_m0300 GSAT_E1_F_M_m0500 GSAT_E1_F_M_p0200 GSAT_E1_F_M_p0300 GSAT_E1_F_M_p0500 GSAT_E2_DFE_M_m0450 GSAT_E2_DFE_M_p0450 GSAT_E2_DFE1_M_m0485 GSAT_E2_DFE1_M_p0485 GSAT_E2_DFE2_M_m0485 GSAT_E2_DFE2_M_p0485 GSAT_E2_F_B_m0600 GSAT_E2_F_B_m0700 GSAT_E2_F_B_p0600 GSAT_E2_F_B_p0700 GSAT_E2_F_T_m0400 GSAT_E2_F_T_m0500 ... GSAT_E2_QT_T_p0385 GSAT_E2_QT1_M_m0450 GSAT_E2_QT1_M_p0450 GSAT_E3_DFE_M_m0450 GSAT_E3_DFE_M_m0485 GSAT_E3_F_T_m0600 GSAT_E3_F_T_p0600 GSAT_E3_KPH_B_m0300 GSAT_E3_KPH_B_p0300 GSAT_E3_KPH_M_m0300 ... GSAT_E6_SWF_T_p0685

64 km/h ODB left

Wall 0° - other loadcases [20ms]

4 3 0 -1 8 14 33 39 0 0 -1 38 38 33 23 23 25 25 18 19 0 1 32 29 10 10 ... 26 -2 29 15 14 15 15 1 12 7 ... 20

4 8 14 33 3 0 -1 39 38 38 33 0 0 -1 23 23 25 25 18 19 32 30 0 0 25 20 ... 16 29 -1 15 14 16 15 13 -1 13 ... 20

26 32 36 33 23 16 3 35 36 38 33 28 24 2 23 21 25 7 18 19 32 30 1 2 25 20 ... 16 29 11 15 14 16 7 13 9 13 ... 20

26 32 36 33 23 16 3 35 36 38 33 28 24 2 23 21 25 7 18 19 32 30 1 2 25 20 ... 16 29 11 15 14 16 7 13 9 13 ... 20

5 4 0 0 10 16 33 39 1 1 0 38 38 33 23 23 25 25 18 19 1 3 32 29 14 15 ... 26 1 29 15 14 16 15 7 12 13 ... 20

5 9 16 33 4 0 0 39 38 38 33 1 1 0 23 23 25 25 18 19 32 30 1 2 25 20 ... 23 29 1 15 14 16 15 13 4 13 ... 20

33 31 21 6 39 37 33 39 38 29 4 38 38 33 23 23 25 25 18 19 4 6 32 29 25 15 ... 26 29 29 15 14 16 15 13 12 13 ... 20

... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ...

20 15 13 10 39 37 33 39 2 1 -3 38 38 33 23 23 25 25 18 19 10 23 32 29 25 20 ... 26 29 29 15 14 16 15 13 12 13 ... 20

38 18 15 18 39 37 33 39 2 1 -2 38 38 33 23 23 25 25 18 19 32 30 32 29 25 20 ... 26 29 29 15 14 16 15 13 12 13 ... 20

38 39 37 33 39 37 33 39 3 2 -1 38 38 33 23 23 25 25 18 19 32 30 32 29 25 20 ... 26 29 29 15 14 16 15 13 12 13 ... 20

... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ...

38 39 37 33 39 37 33 39 38 38 33 38 38 33 23 23 25 25 18 19 32 30 32 29 25 20 ... 26 29 29 15 14 16 15 13 12 13 ... 20

12.2 Classification of Wall 0° Load Cases

215

Figure 12.1: Model description of classification algorithm of Wall 0° load cases

case group is not possible because the second criterion of a positive separation of the Delta CI values cannot be fulfilled. In order to find further classification algorithms for the other load cases, there are two different approaches according to the model shown in Chapter 5: • Multi-stage algorithm • Time-dependent algorithm Since, according to the research questions, a single-stage solution is sought, the further classification algorithms attempt to select the application thresholds in such a way that the Wall 0° load cases are to be separated by a temporal differentiation within the framework of the classification algorithms. Assuming that only one classification is permissible for each load case and that all other algorithms are terminated after this classification, this leads to the dependencies shown in Figure 12.2. Using the example of the ODB load cases, the solution is shown here, taking into account the temporal relationships between the different classifications. The time sequence of the classification algorithms must be such that the classification of the Wall 0° load cases in the Wall 0° classification algorithm is completed before another load case is classified in a different classification algorithm. In the ODB classification algorithms the Wall 0° load cases may also exceed the respective threshold, but only after they have been identified as Wall 0° load

216

Chapter 12 Algorithm Concept for the Classification of Load Cases

Figure 12.2: Time-dependent classification algorithms

cases in their own algorithm. This procedure prevents a misclassification of the Wall 0° load cases, and the unintentional crossing of the thresholds in the ODB classification algorithm does not lead to a misclassification. With the exception of the ODB load cases, all other load cases must be permanently below the threshold. This methodology of time-dependent algorithms is used for all further classification algorithms.

12.3 Classification of ODB Load Cases The ODB load cases are classified according to the methodology presented, taking into account the temporal dependencies to the Wall 0° classification algorithm. The discriminant analysis was used to identify the sensors highlighted in Table 12.8 for the times 30 ms and 40 ms according to the ODB firing time requirements. The Wall 0° load cases can be disregarded for the Delta CI value analysis.

12.3 Classification of ODB Load Cases

217

Table 12.8: Wilks’ Lambda for discrimination of ODB load cases at 30 ms and 40 ms

Sensor

Wilks’ Lambda

Sensor

Wilks’ Lambda

ODB - other Load case (40 ms)

Ranking

ODB - other Load case (30 ms)

1 2 3 4 5 6 7 8 9 10 11 ... 70 71

GSAT_E2_QT_B_m0200 GSAT_E2_QT_B_00000 GSAT_E2_QT_B_p0200 GSAT_E1_F_B_00000 GSAT_E1_F_B_m0100 GSAT_E1_F_B_m0300 GSAT_E1_F_B_p0100 GSAT_E1_F_B_p0300 GSAT_E2_QT_B_m0300 GSAT_E2_QT1_M_m0450 GSAT_E3_KPH_B_m0300 ... GSAT_E5_RI_M_p0500 GSAT_E6_SWF_T_p0685

0.27 0.43 0.46 0.51 0.52 0.54 0.59 0.70 0.70 0.72 0.72 ... -

GSAT_E2_QT_B_m0200 GSAT_E2_QT_B_00000 GSAT_E2_QT_B_p0200 GSAT_E1_F_B_m0100 GSAT_E1_F_B_00000 GSAT_E1_F_B_m0300 GSAT_E1_F_B_p0100 GSAT_E2_QT_B_m0300 GSAT_E2_QT1_M_m0450 GSAT_E3_KPV_B_00000 GSAT_E2_QT_M_m0385 ... GSAT_E5_RI_M_p0500 GSAT_E6_SWF_T_p0685

0.31 0.47 0.52 0.60 0.60 0.61 0.67 0.70 0.72 0.73 0.74 ... -

If the eight sensors with the greatest Wilks’ Lambda values are considered with respect to their discriminant direction, then the result is as shown in Table 12.9 and Table 12.10. The discriminant against the other load cases is considered at each of the two selected times. Due to the left/right dependence of the ODB load cases, the two sides are considered separately, and the weakest ODB load cases (40 km/h) are compared with the other load cases as described. The respective values of the individual sensors are considered holistically according to the algorithm approach in which the sensor values are also added to the CITn. The overall result of the addition of the sensors for each load case is decisive for the suitability of the sensors for discrimination. If the Delta CI values of the selected sensors are considered over the various points in time, then they all add up to a positive overall result. The negative values are distributed in such a way that no sensor proves to be unsuitable across all points in time and when considering the respective mirrored sensor partner. With the selected eight sensors, an application for classifying the ODB load cases can be created.

218

Chapter 12 Algorithm Concept for the Classification of Load Cases

Table 12.9: Analysis of the Delta CI values for the ODB classification at 30 ms

Sensor: GSAT_

40 km/h Wall 30° left

40 km/h Wall 30° right

32 km/h Wall 30° left

32 km/h Wall 30° right

35 km/h Pole

30 km/h Truck

80 km/h Wildlife left

70 km/h Wildlife left

60 km/h Wildlife left

...

10 km/h RCAR right

ODB right - other Load cases [30 ms]

E1_F_B_00000 E1_F_B_m0100 E1_F_B_m0300 E1_F_B_p0100 E1_F_B_p0300 E2_QT_B_00000 E2_QT_B_m0200 E2_QT_B_p0200

19 12 -17 17 15 0 32 -32

19 24 30 17 15 0 32 -32

26 23 15 33 52 0 11 0

25 39 47 26 23 0 32 -21

-3 1 47 3 52 -17 32 0

47 47 47 49 52 0 32 0

13 7 7 49 52 0 32 0

47 10 9 49 52 0 32 0

47 47 47 49 52 0 32 0

... ... ... ... ... ... ... ...

47 47 47 49 52 0 32 0

Total

46

105

160

171

115

274

160

199

274

...

274

Sensor: GSAT_

40 km/h Wall 30° left

40 km/h Wall 30° right

32 km/h Wall 30° left

32 km/h Wall 30° right

35 km/h Pole

30 km/h Truck

80 km/h Wildlife left

70 km/h Wildlife left

60 km/h Wildlife left

...

10 km/h RCAR right

ODB left - other Load cases [30 ms]

E1_F_B_00000 E1_F_B_m0100 E1_F_B_m0300 E1_F_B_p0100 E1_F_B_p0300 E2_QT_B_00000 E2_QT_B_m0200 E2_QT_B_p0200

19 26 35 3 -37 0 0 -1

19 26 35 3 -37 0 0 -1

26 25 20 19 0 0 -21 31

25 41 52 12 -29 0 0 10

-3 3 52 -11 0 -17 0 31

47 49 52 35 0 0 0 31

13 9 12 35 0 0 0 31

47 12 14 35 0 0 0 31

47 49 52 35 0 0 0 31

... ... ... ... ... ... ... ...

47 49 52 35 0 0 0 31

Total

45

45

100

111

55

214

100

139

214

...

214

12.3 Classification of ODB Load Cases

219

Table 12.10: Analysis of the Delta CI values for the ODB classification at 40 ms

Sensor: GSAT_

40 km/h Wall 30° left

40 km/h Wall 30° right

32 km/h Wall 30° left

32 km/h Wall 30° right

35 km/h Pole

30 km/h Truck

80 km/h Wildlife left

70 km/h Wildlife left

60 km/h Wildlife left

...

10 km/h RCAR right

ODB right - other Load cases [40 ms]

E1_F_B_00000 E1_F_B_m0100 E1_F_B_m0300 E1_F_B_p0100 E1_F_B_p0300 E2_QT_B_00000 E2_QT_B_m0200 E2_QT_B_p0200

19 12 -43 17 15 0 58 -58

19 24 30 17 15 0 58 -58

26 23 15 33 78 0 11 0

25 39 73 26 23 0 58 -47

-3 1 67 3 73 -43 58 -10

73 73 73 75 78 0 58 0

13 7 7 75 78 0 58 0

73 10 9 75 78 0 58 0

73 73 73 75 78 0 58 0

... ... ... ... ... ... ... ...

73 73 73 75 78 0 58 0

Total

20

105

186

197

146

430

238

303

430

...

430

Sensor: GSAT_

40 km/h Wall 30° left

40 km/h Wall 30° right

32 km/h Wall 30° left

32 km/h Wall 30° right

35 km/h Pole

30 km/h Truck

80 km/h Wildlife left

70 km/h Wildlife left

60 km/h Wildlife left

...

10 km/h RCAR right

ODB left - other Load cases [40 ms]

E1_F_B_00000 E1_F_B_m0100 E1_F_B_m0300 E1_F_B_p0100 E1_F_B_p0300 E2_QT_B_00000 E2_QT_B_m0200 E2_QT_B_p0200

19 26 35 3 -63 0 0 -1

19 26 35 3 -63 0 0 -1

26 25 20 19 0 0 -47 57

25 41 78 12 -55 0 0 10

-3 3 72 -11 -5 -43 0 47

73 75 78 61 0 0 0 57

13 9 12 61 0 0 0 57

73 12 14 61 0 0 0 57

73 75 78 61 0 0 0 57

... ... ... ... ... ... ... ...

73 73 73 61 0 0 0 57

Total

19

19

100

111

60

344

152

217

344

...

337

220

Chapter 12 Algorithm Concept for the Classification of Load Cases

Diagram 12.3: Classification of ODB load cases at an amplitude variation of -15%

Diagram 12.3 shows the created application threshold using the example of the 15% amplitude variation. The Wall 0° load cases are shown in dark blue, the ODB load cases in light blue and all other load cases in green. If the classification times of the ODB load cases are considered, then the result shown in Table 12.11 is obtained. Table 12.11: Classification times of the ODB load cases FTR [ms]

Load case

64 km/h ODB left 64 km/h ODB right 56 km/h ODB left 56 km/h ODB right 40 km/h ODB left 40 km/h ODB right

CT [ms]

-15%

nom.

+15%

- 15%

nom.

+15%

30.5 30.5 32,5 32.5 42 41

28,5 29 30,5 30.5 38.5 38.5

26.5 27 29 29 35.5 34.5

15.5 15,5 16.5 16,5 21,5 21.5

14 14 15,5 15,5 20,5 20,5

13 13 13.5 13.5 17.5 17

All ODB load cases are classified correctly between 13 ms and 21.5 ms for the nominal signals and for the ±15% amplitude variation. For all load cases, the classification is therefore ahead of the times of their firing time requirements. If the times in which the Wall 0° load cases exceed the ODB threshold are

12.4 Classification of Further Load Case Groups

221

Figure 12.3: ODB classification model

considered, then these are between 10.5 ms and 22 ms, as shown in Table 12.12. This prevents misclassification, as they were classified correctly much earlier in the Wall 0° algorithm. Table 12.12: Classification times of Wall 0° load cases in the ODB and the Wall 0° classification Load case

56 km/h Wall 0° 50 km/h Wall 0° 40 km/h Wall 0° 32 km/h Wall 0° 27 km/h Wall 0°

CT [ms] (Wall 0° algorithm)

CT [ms] (ODB algorithm)

-15%

nom.

+15%

-15%

nom.

+15%

3.5 3.5 3.5 3.5 4

3 3 3.5 3.5 3.5

3 3 3 3.5 3.5

13.5 14 19 22 22

11.5 13.5 14.5 17.5 19

10.5 10.5 13 15.5 16

The result is thus a single-stage, time-limited ODB classification algorithm according to Figure 12.3.

12.4 Classification of Further Load Case Groups For all other load case groups, the discriminant analysis and the Delta CI value consideration can be carried out according to the same methodology. The results are presented in Table 12.13.

222

Chapter 12 Algorithm Concept for the Classification of Load Cases

Table 12.13: Sensor selection for Pole, Truck and Wall 30° classification Sensors Truck load case

Sensors Pole load case

Sensors Wall 30° load case

GSAT_E3_KPV_T_00000 GSAT_E2_QT_T_m0385 GSAT_E2_F_T_m0400 GSAT_E2_F_T_p0400 GSAT_E2_QT_M_m0385 GSAT_E2_QT_T_p0385 GSAT_E2_F_T_m0500 GSAT_E5_SWF_T_m0575 GSAT_E5_SWF_T_p0575 GSAT_E2_F_T_p0500 GSAT_E2_QT_M_p0385

GSAT_E1_F_B_00000 GSAT_E1_F_M_00000 GSAT_E3_KPV_B_00000 GSAT_E3_KPV_M_00000 GSAT_E3_KPV_T_00000

GSAT_E2_DFE_M_m0450 GSAT_E2_DFE_M_p0450 GSAT_E2_DFE1_M_m0485 GSAT_E2_DFE1_M_p0485 GSAT_E2_DFE2_M_m0485 GSAT_E2_DFE2_M_p0485 GSAT_E2_QT_M_m0300 GSAT_E2_QT_M_m0400 GSAT_E2_QT_M_p0300 GSAT_E2_QT_M_p0400

In the further investigations, all sensors shown in Table 12.13 are shown to be suitable for their respective classification algorithm. The result of the application is shown in the following three diagrams Diagram 12.4, Diagram 12.5 and Diagram 12.6. The respective load cases to be classified are shown in their colours, all other load cases in green. Table 12.14 shows the classification times for the three classification algorithms. All load cases are classified correctly well before their required firing times. Table 12.15 shows the threshold exceedance times of the Wall 0° load cases. In all three classification algorithms, they are significantly greater than the classification times in the Wall 0° algorithm. The application could thus be performed correctly for all crash type classifications according to the model in Figure 12.3. Table 12.14: Classification times of Pole, Wall 30° and Truck load cases FT [ms]

Load case

35 km km/h Pole 40 km/h Wall 30° left 40 km/h Wall 30° right 32 km/h Wall 30° left 32 km/h Wall 30° right 30 km km/h Truck

CT Time [ms]

-15%

nom.

+15%

-15%

nom

+15%

40 34 35.5 44 47 61

38 26.5 27 43 41 47

37 25 25 35.5 35.5 41

27.5 15.5 15.5 16.5 16.5 32.5

27 14 14 15.5 15.5 31.5

26.5 13 13 13.5 13.5 31

12.4 Classification of Further Load Case Groups

223

Diagram 12.4: Classification of the Pole load case for nominal signals

Diagram 12.5: Classification of the Wall 30° load cases for nominal signals and ±15% amplitude variation

224

Chapter 12 Algorithm Concept for the Classification of Load Cases

Diagram 12.6: Classification of the Truck load case +15% amplitude variation Table 12.15: Threshold exceedance times for Wall 0° load cases in the Wall 0° and the other classification algorithm Algorithm

Load case

CT [ms] (Wall 0° algorithm)

CT [ms] (other algorithm)

-15%

nom.

+15%

-15%

nom.

+15%

Pole

56 km/h Wall 0° 50 km/h Wall 0° 40 km/h Wall 0° 32 km/h Wall 0° 27 km/h Wall 0°

3.5 3.5 3.5 3.5 4

3 3 3.5 3.5 3.5

3 3 3 3.5 3.5

13.5 14 19 22 22

11.5 13.5 14.5 17.5 19

10.5 10.5 13 15.5 16

Wall 30°

56 km/h Wall 0° 50 km/h Wall 0° 40 km/h Wall 0° 32 km/h Wall 0° 27 km/h Wall 0°

3.5 3.5 3.5 3.5 4

3 3 3.5 3.5 3.5

3 3 3 3.5 3.5

13.5 14 19 22 22

11.5 13.5 14.5 17.5 19

10.5 10.5 13 15.5 16

Truck

56 km/h Wall 0° 50 km/h Wall 0° 40 km/h Wall 0° 32 km/h Wall 0° 27 km/h Wall 0°

3.5 3.5 3.5 3.5 4

3 3 3.5 3.5 3.5

3 3 3 3.5 3.5

13.5 14 19 22 22

11.5 13.5 14.5 17.5 19

10.5 10.5 13 15.5 16

12.6 Summary

225

12.5 Classification of the Hit Direction Classification of the hit direction differs from the previous classifications in that it is carried out across load case groups. The procedure follows the same methodology. Table 12.16 shows the selected sensors for the hit positions on the left and right. In contrast to the classification of the crash type, the sensors used here no longer are sensor pairs consisting of a sensor on the left and the corresponding sensor on the right. For the hit position on the left, sensors from the left to middle area of the front end are preferred, for the hit position on the right, sensors from the right to middle area are preferred. The discriminant analysis has shown that, for both hit positions, the same but mirrored sensors were the most suitable. Table 12.16: Sensor selection for classification of left and right hit position Left load cases

Right load cases

GSAT_E1_F_M_m0500 GSAT_E1_F_M_m0300 GSAT_E2_F_B_m0600 GSAT_E2_F_B_m0700 GSAT_E1_F_M_m0200

GSAT_E1_F_M_p0500 GSAT_E1_F_M_p0300 GSAT_E2_F_B_p0600 GSAT_E2_F_B_p0700 GSAT_E1_F_M_p0200

The classification times for the hit position are as shown in Table 12.17. The result of the application is shown in Diagram 12.7 and Diagram 12.8 The directional classification is also in line with the model shown in Figure 12.3.

12.6 Summary The previous chapter showed that classification algorithms for crash type and direction could be developed on the basis of 71 sensors. Due to the dominance of the Wall 0° load cases, only the Wall 0° classifications could be implemented independent of time. All other classifications are time-limited. It could be shown that the previously formulated requirements on time-limited algorithms are fulfilled. Together with the algorithm for the firing decision, this results in the overall picture shown in Figure 12.4 in which all algorithms are described according to the algorithm evaluation model.

226

Chapter 12 Algorithm Concept for the Classification of Load Cases

Diagram 12.7: Classification of load cases with left hit position

Diagram 12.8: Classification of load cases with right hit position

12.6 Summary

227

Table 12.17: Classification times for left and right hit position FT [ms]

Load case

CT [ms]

-15%

nom.

+15%

-15%

nom

+15%

64 km/h ODB left 40 km/h Wall 30° left 56 km/h ODB left 32 km/h Wall 30° left 40 km/h ODB left 80 km/h Wildlife left 70 km/h Wildlife left 60 km/h Wildlife left

30.5 32.5 32.5 45.5 42 -

28.5 29.5 30.5 36.5 38.5 -

26.5 28.5 29 33.5 35.5 -

14 19.5 14.5 23 16 16.5 19.5 20

13.5 19 14 21.5 15.5 16.5 19.5 20

13.5 18.5 13.5 21 15 15 17 19.5

64 km/h ODB right 40 km/h Wall 30° right 56 km/h ODB right 32 km/h Wall 30° right 40 km/h ODB right 80 km/h Wildlife right 70 km/h Wildlife right 60 km/h Wildlife right

30.5 32.5 32.5 41.5 41 -

29 30 30.5 36.5 38.5 -

27 29 29 33.5 34.5 -

19.5 25.5 20.5 30.5 22 23 29.5 30

19.5 25 20 30 21.5 22.5 26.5 30

19.5 24.5 19.5 27.5 21 21 23 29.5

In line with the requirements, all algorithms could be designed single-stage. All seven instances, one for the main algorithm and six for the classification algorithms, could be represented in line with the requirements. Time-independent implementation was possible for the main algorithm and for the classification algorithm of the Wall 0° load cases. All other classification algorithms can only be implemented in a time-dependent manner due to the dominant Wall 0° load cases. The requirements formulated in Section 4.3.2 for the implementation of the CI algorithm are thus fully met. If the results presented in the preceding chapters are evaluated in detail, the picture shown in Figure 12.5 is obtained. Both for nominal signals and for the amplitude variations investigated, the applications created on the basis of the CITn values show the desired relationship between firing time requirements and measured values. The additional requirements for a classification of the load cases according to type and direction could also be fulfilled according to the specifications. In addition, the firing algorithm shows very good stability against NoFire / Miusse load cases.

228

Chapter 12 Algorithm Concept for the Classification of Load Cases

Figure 12.4: Classification model of all algorithms for 71 sensors

Figure 12.5: CI algorithm compared to stated requirements from Section 4.3.2

13 Two-Stage Algorithm to Minimize the Number of Sensors In the preceding chapters, an algorithm concept was developed which, on the basis of sensors distributed locally in the front end, enables a robust algorithm concept for the timely triggering of the restraint systems and for the classification according to load case groups. The selection of the sensors was optimized to meet all requirements that result from the use of a single-stage algorithm concept. In this chapter, another algorithm is presented which is based on the same principle of action involving local component-specific accelerations. The aim of this concept is to reduce the number of sensors to a minimum. To this end, a multi-stage approach is to be used instead of a single-stage algorithm concept.

13.1 Methodology In Chapter 11 it was shown that, with 71 sensors, all considered load cases could be brought to timely firing using a firing threshold based on CIT values. A twostage concept opens up further degrees of freedom by resolving the continuous relationship between CIT values and trigger threshold for all load cases over time. The aim is to identify load cases that prevent a reduction to a smaller number of sensors due to these load cases no longer being integratable in an overall correlation between firing time requirements and CIT values. For the identified load cases, a possibility must be found to influence their triggering thresholds in such a way that timely triggering on the basis of the reduced sensor set is possible. For this purpose, it is necessary to classify these load cases in a timely manner and thus separate them from the rest of the load case spectrum. The prerequisites for this type of application are shown in Figure 13.1. The two load cases Pole and Truck, for which a threshold adjustment is to be implemented, are shown here as examples. According to the results from the previous chapters, it is assumed that, with a reduced sensor set, the Wall 0° load © Springer Fachmedien Wiesbaden GmbH, part of Springer Nature 2020 A. Leschke, Algorithm Concept for Crash Detection in Passenger Cars, https://doi.org/10.1007/978-3-658-29392-5_13

230

Chapter 13 Two-Stage Algorithm to Minimize the Number of Sensors

Figure 13.1: Requirements on a two-stages firing time algorithm

cases still remain dominant over the other load case groups. It therefore remains one of the essential prerequisites that the Wall 0° load cases have triggered safely before they are classified incorrectly in the Truck or Pole classification. If this is the case, then a threshold adjustment can be carried out in the main algorithm on the basis of the Pole and Truck classification. Its aim is to achieve the required firing times for these load cases. It is important that an incorrect evaluation of other load cases can be reliably prevented in the Pole and Truck classifications. If these conditions are met, then the firing algorithm can be implemented as shown in Figure 13.2.

13.1 Methodology

Figure 13.2: Two-stages firing algorithm

231

232

Chapter 13 Two-Stage Algorithm to Minimize the Number of Sensors

The firing algorithm consists of two levels. This is shown in Figure 13.2. The main algorithm makes its decision based on the CITn values of sensors 1 to n. As long as the load case under consideration is not classified as a Pole or Truck load case, a firing decision must be made on the basis of the first threshold. As soon as the load case is classified as Truck or Pole, threshold 2 is used, which then ensures the timely triggering of these two load cases. This procedure can be used to ensure timely firing for all load cases in the load case set. At the same time, the number of sensors can be significantly reduced once again.

13.2 Maximum Reduction of Sensors As already shown in the previous chapters, the load cases Pole and Truck differ clearly from the other Fire load cases due to their hit position and their barrier characteristics. A significant proportion of the 71 sensors that had been identified in the previous chapters as being sufficient to meet all requirements is required exclusively to differentiate between these two load cases and the others. The following section shows how a significant reduction in the number of sensors can be achieved by removing these two load cases from the load case set and how the correct firing decision for these two load cases can then be ensured by classification. If the methodology introduced in Section 10.3 is applied to the reduced load case set, then a sensor set reduced to 17 sensors can be found that meets all requirements on a correlation between firing time and CIT values. The remaining sensors are shown in Figure 13.3. When data quality is assessed using the methodology introduced in Chapter 9, the result is as shown in Table 13.1 and Diagram 13.1. If the results of the reduced load case set are compared in the dendrogram and in the Mojena evaluation, then it can be seen that, with a full load case set, similar results can be achieved for 219 sensors and for 71 sensors. A good separation capability can be expected by removing the two load cases Pole and Truck. The result is reflected in the correlation comparison of the CITn values to the respective firing time requirements that is shown in Table 13.2.

13.2 Maximum Reduction of Sensors

233

Figure 13.3: Reduced sensor pool for a two-stage algorithm Table 13.1: Evaluation of load case separability according to Mojena Cluster

10 9 8 7 6 5 4 3 2 1

All Load cases

Fire Load cases

Error sum of squares

Mojena criteria

Error sum of squares

Mojena criteria

1.60 2.11 2.28 2.71 3.03 3.30 3.59 3.72 5.04 21.05

0.080 0.123 0.237 0.323 0.396 0.472 0.507 0.858 5.112

2.14 2.76 2.85 3.20 3.97 4.68 4.68 4.98 6.49 10.13

-0.134 -0.112 -0.029 0.151 0.318 0.318 0.388 0.743 1.598

234

Chapter 13 Two-Stage Algorithm to Minimize the Number of Sensors

Table 13.2: MCITn values for 17 sensors MCITn / [ms]

LC 10

13

16

17

20

28

32

35

39

41

51

56W 50W 40W 32W 27W 64Ol 64Or 40Wl 40Wr 56Or 56Or 32Wl 32Wr 40Ol 40Or 35P 30T

4.7 3.6 1.8 0.5 0.0 1.0 1.1 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0

8.9 7.4 4.5 2.6 1.2 1.9 1.9 0.2 0.2 0.0 0.0 0.0 0.0 0.0 0.0 1.2 0.3

14.4 12.4 8.2 4.9 3.4 3.0 3.0 0.5 0.5 0.4 0.4 0.0 0.0 0.0 0.0 2.6 1.4

16.2 14.2 9.8 6.1 4.1 3.4 3.4 0.6 0.6 0.5 0.5 0.0 0.0 0.0 0.0 3.2 1.7

22.2 19.9 14.9 9.9 6.8 4.8 4.4 1.5 1.5 0.8 0.8 0.0 0.0 0.0 0.0 5.4 2.8

38.2 35.6 29.9 23.5 18.1 9.9 9.8 6.7 6.7 2.4 1.9 0.5 0.2 0.2 0.1 12.1 5.6

46.2 43.6 37.5 30.6 25.2 13.7 13.5 11.2 11.2 4.4 4.2 1.5 1.2 0.6 0.6 17.1 7.5

52.2 49.6 43.1 35.9 30.5 16.5 16.6 14.7 14.7 6.2 5.9 2.5 2.3 1.0 0.9 20.9 9.2

60.2 57.6 50.6 42.9 37.5 20.9 20.9 19.6 19.6 8.7 8.8 4.0 4.1 1.5 1.4 26.1 11.6

64.2 61.6 54.4 46.5 41.1 23.6 23.4 22.5 22.5 10.1 10.7 5.0 5.3 2.0 1.8 28.7 12.8

84.2 81.6 73.5 65.4 59.9 39.1 37.4 39.1 39.1 20.8 22.8 12.0 12.3 6.5 5.9 41.6 18.6

NM

0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0

0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0

0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0

0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0

0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0

0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0

0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0

0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0

0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0

0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0

0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0

13.2 Maximum Reduction of Sensors

235

Diagram 13.1: Dendrograms with reduced sensor set

The required correlation is very stable over all firing times considered. According to the methodology shown in Chapter 11, an application threshold can be determined for the load cases with the exception of the Pole and Truck load cases. The respective threshold sections are determined via two regression evaluations (Diagram 13.2 and Diagram 13.3) and are connected appropriately, taking into account the amplitude variation ±15%. Furthermore, a sufficient separation from the NoFire and Misuse load cases must be ensured. The reduction to 17 sensors has significantly reduced NoFire / Misuse sensitivity, as shown in Table 13.2. Even the fast wildlife accidents show almost no signals with the 17 remaining sensors. This makes the application even more robust than the design with 71 sensors. The removal of the Pole and Truck load cases also has a positive effect here. The application line shown in Diagram 13.4 can be generated on the basis of the regression line determined in this way. In the diagram, it can be seen very well that the load cases with a fast progress of the crash severity trigger in the first threshold section and the load cases with a slow progress of the crash severity in the second threshold section. As shown in Table 13.3 the firing time requirements of all load cases, both for nominal signals and with amplitude variation, are fully met with the reduced sensor set.

236

Chapter 13 Two-Stage Algorithm to Minimize the Number of Sensors

Diagram 13.2: Regression line, load cases with fast crash severity progress

Diagram 13.3: Regression line, load cases with slow crash severity progress (without Pole and Truck)

13.2 Maximum Reduction of Sensors

237

Diagram 13.4: Application for 17 sensors for nominal signals (without Truck and Pole) Table 13.3: Firing times with nominal signal and amplitude variation of ±15% FTR [ms]

Firing time [ms]

Deviation [%]

Firing time [ms]

Deviation [%]

Firing time [ms]

Deviation [%]

+15% signal

max.

nominal signal

nom.

56 km/h Wall 0° 50 km/h Wall 0° 40 km/h Wall 0° 32 km/h Wall 0° 27 km/h Wall 0° 64 km/h ODB l. 64 km/h ODB r. 40 km/h Wall 30° l. 40 km/h Wall 30° r. 56 km/h ODB l. 56 km/h ODB r. 32 km/h Wall 30° l. 32 km/h Wall 30° r. 40 km/h ODB l. 40 km/h ODB r.

-15% signal

min.

Load case

8 10.4 12.8 13.6 16 25.2 25.2 28.8 28.8 31.5 31.5 35.1 35.1 36.9 36.9

10 13 16 17 20 28 28 32 32 35 35 39 39 41 41

12 15.6 19.2 20.4 24 33.6 33.6 38.4 38.4 42 42 46.8 46.8 49.2 49.2

11.5 12.5 15 18 23.5 33 33 35 34 36.5 37.5 43 43 41.5 41.5

15.0 -3.8 -6.3 5.9 17.5 17.9 17.9 9.4 6.3 4.3 7.1 10.3 10.3 1.2 1.2

11 12 15 18 20.5 29 29 29.5 30.5 35.5 35.5 39 39 41.5 42

10.0 -7.7 -6.3 5.9 2.5 3.6 3.6 -7.8 -4.7 1.4 1.4 0.0 0.0 1.2 2.4

11 12 14.5 17.5 20.5 27.5 28.5 28.5 28.5 30 30 34 36 40.5 40.5

10.0 -7.7 -9.4 2.9 2.5 -1.8 1.8 -10.9 -10.9 -14.3 -14.3 -12.8 -7.7 -1.2 -1,2

Ø +7.6

Ø +0.4

Ø -5.0

238

Chapter 13 Two-Stage Algorithm to Minimize the Number of Sensors

13.3 Classification of Pole and Truck Load Cases In order to trigger the two remaining load cases in a timely manner, they must be classified in accordance with the general conditions described at the beginning of this chapter. Pole load case On the basis of a discriminant analysis with subsequent Delta CI value evaluation (Table 13.5), the sensors determined in Table 13.4 were selected for the classification of the Pole load case. Table 13.4: Wilks’ Lambda values for the discrimination of the Pole load case

Sensor

Wilks’ Lambda

Sensor

Wilks’ Lambda

Pole - other loadcases (40 ms)

Ranking

Pole - other loadcases (30 ms)

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17

GSAT_E3_KPV_M_00000 GSAT_E1_F_T_00000 GSAT_E2_QT_M_m0100 GSAT_E2_QT_M_p0100 GSAT_E2_QT_M_00000 GSAT_E1_F_T_m0200 GSAT_E1_F_T_p0200 GSAT_E3_KPV_M_p0250 GSAT_E3_KPH_M_p0300 GSAT_E2_QT2_M_p0450 GSAT_E2_DFE_M_p0450 GSAT_E3_KPH_M_m0300 GSAT_E3_DFE_M_p0485 GSAT_E3_KPV_M_m0250 GSAT_E2_DFE_M_m0450 GSAT_E2_QT2_M_m0450 GSAT_E3_DFE_M_m0485

0.01 0.54 0.65 0.72 0.79 0.87 0.88 0.96 1.00 1.00 1.00 1.00 1.00 1.00 1.00 1.00 -

GSAT_E2_QT_M_m0100 GSAT_E3_KPV_M_00000 GSAT_E1_F_T_00000 GSAT_E2_QT_M_p0100 GSAT_E2_QT_M_00000 GSAT_E1_F_T_m0200 GSAT_E3_KPV_M_p0250 GSAT_E1_F_T_p0200 GSAT_E3_KPV_M_m0250 GSAT_E3_KPH_M_m0300 GSAT_E3_KPH_M_p0300 GSAT_E2_QT2_M_p0450 GSAT_E2_DFE_M_p0450 GSAT_E3_DFE_M_p0485 GSAT_E2_QT2_M_m0450 GSAT_E2_DFE_M_m0450 GSAT_E3_DFE_M_m0485

0.67 0.68 0.69 0.74 0.79 0.85 0.86 0.86 0.88 0.91 0.95 0.99 1.00 1.00 1.00 1.00 -

On the basis of the four identified sensors, an application for classification was created, which ensures the timely separation of the Pole load case from all other load cases and the timely threshold crossing for the Wall 0° load cases. Diagram 13.5 shows the application for the nominal signals. Table 13.6 shows the corresponding classification times. The Pole load case is detected reliably at 26 ms. The Wall 0° load cases are detected clearly after their

13.3 Classification of Pole and Truck Load Cases

239

Table 13.5: Analysis of the Delta CI values for the Pole load case at 30 ms and 40 ms

Sensor: GSAT_:

64 km/h ODB left

64 km/h ODB right

40 km/h Wall 30° left

40 km/h Wall 30° right

56 km/h ODB left

56 km/h ODB right

32 km/h Wall 30° left

32 km/h Wall 30° right

...

10 km/h RCAR right

Pole - other LC 30 ms

E3_KPV_M_00000 E1_F_T_00000 E2_QT_M_m0100 E2_QT_M_p0100

25 35 -10 15

22 32 15 14

35 32 15 14

35 35 35 15

35 35 35 15

35 35 35 15

35 35 35 15

35 35 35 15

... ... ... ...

35 35 35 15

Summe

65

83

96

120

120

120

120

120

...

120

Sensor: GSAT_:

64 km/h ODB left

64 km/h ODB right

40 km/h Wall 30° left

40 km/h Wall 30° right

56 km/h ODB left

56 km/h ODB right

32 km/h Wall 30° left

32 km/h Wall 30° right

...

10 km/h RCAR right

Pole - other LC 40 ms

E3_KPV_M_00000 E1_F_T_00000 E2_QT_M_m0100 E2_QT_M_p0100

25 -10 58 15

22 59 -10 16

60 32 15 14

60 32 15 14

35 61 61 20

36 61 61 31

61 61 61 41

61 61 61 41

... ... ... ...

61 61 61 41

Summe

88

87

121

121

177

189

224

224

...

224

240

Chapter 13 Two-Stage Algorithm to Minimize the Number of Sensors

Diagram 13.5: Classification of the Pole load case with nominal signal values

own firing times from Table 13.3. Thus, the requirements on the classification of the Pole load case are fulfilled. Table 13.6: Classification times of the Pole load case and the Wall 0° load cases in the Pole classification algorithm CT [ms]

Load case

56 km/h Wall 0° 50 km/h Wall 0° 40 km/h Wall 0° 32 km/h Wall 0° 27 km/h Wall 0° 35 km/h Pole

-15%

nom.

+15%

19.5 20,5 22 24.5 26

19.5 20 22 24.5 26

19.5 20 21.5 24 26

26

26

26

Truck load case On the basis of a discriminant analysis with subsequent Delta CI value evaluation (Table 13.8), the sensors determined in Table 13.7 were selected for the classification of the Truck load case. On the basis of the three identified sensors, a classification application can be created which ensures the timely separation of the Truck load case from all other

13.3 Classification of Pole and Truck Load Cases

241

Table 13.7: Wilks’ Lambda values for discrimination of the Truck load case

Sensor

Wilks’ Lambda

Sensor

Wilks’ Lambda

Truck - other LC (50ms)

Ranking

Truck - other LC (40ms)

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17

GSAT_E1_F_T_00000 GSAT_E1_F_T_p0200 GSAT_E1_F_T_m0200 GSAT_E3_KPV_M_p0250 GSAT_E2_QT_M_p0100 GSAT_E2_QT_M_00000 GSAT_E3_KPH_M_p0300 GSAT_E2_QT2_M_p0450 GSAT_E2_DFE_M_p0450 GSAT_E2_QT_M_m0100 GSAT_E3_KPH_M_m0300 GSAT_E3_DFE_M_p0485 GSAT_E3_KPV_M_m0250 GSAT_E3_KPV_M_00000 GSAT_E2_DFE_M_m0450 GSAT_E2_QT2_M_m0450 GSAT_E3_DFE_M_m0485

0.60 0.75 0.76 0.99 0.99 0.99 1.00 1.00 1.00 1.00 1.00 1.00 1.00 1.00 1.00 1.00 -

GSAT_E1_F_T_00000 GSAT_E1_F_T_m0200 GSAT_E1_F_T_p0200 GSAT_E3_KPH_M_m0300 GSAT_E3_KPH_M_p0300 GSAT_E3_KPV_M_00000 GSAT_E3_KPV_M_p0250 GSAT_E3_KPV_M_m0250 GSAT_E2_QT_M_00000 GSAT_E2_QT2_M_p0450 GSAT_E2_QT_M_p0100 GSAT_E2_QT_M_m0100 GSAT_E2_DFE_M_p0450 GSAT_E3_DFE_M_p0485 GSAT_E2_QT2_M_m0450 GSAT_E2_DFE_M_m0450 GSAT_E3_DFE_M_m0485

0.71 0.79 0.80 0.95 0.96 0.99 0.99 0.99 0.99 0.99 0.99 0.99 1.00 1.00 1.00 1.00 -

load cases and the timely threshold crossing for the Wall 0° load cases. Diagram 13.6 shows the application for the nominal signals. Table 13.9 shows the corresponding classification times. The Truck load case is detected reliably at 25 ms. The Wall 0° load cases are detected clearly after their own firing times from Table 13.3. Thus, the requirements on the classification of the Truck load case are fulfilled. The classification of the Truck load case is possible at 25 ms over the entire amplitude variation. The Wall 0° load cases would theoretically be classified incorrectly between 18.5 ms and 25.5 ms. However, all these times are clearly after their trigger times in the main algorithm. In the main algorithm, the triggering times are between 10.5 ms for the 56 km/h Wall 0° load case and 23 ms for the 27 km/h Wall 0° load case. A timely classification is thus possible.

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Chapter 13 Two-Stage Algorithm to Minimize the Number of Sensors

Table 13.8: Analysis of the Delta CI values for the Truck load case at 40 ms and 50 ms

Sensor: GSAT_:

64 km/h ODB left

64 km/h ODB right

40 km/h wall 30° left

40 km/h wall 30° right

56 km/h ODB left

56 km/h ODB right

32 km/h wall 30° left

32 km/h wall 30° right

...

10 km/h RCAR right

Truck - other LC (40 ms)

E1_F_T_00000 E1_F_T_p0200 E1_F_T_m0200

23 -3 31

20 31 -2

33 31 31

33 31 31

33 1 31

33 31 1

33 31 31

33 31 31

... ... ..

33 31 31

Summe

51

49

95

95

65

65

95

95

...

95

Sensor: GSAT_:

64 km/h ODB left

64 km/h ODB right

40 km/h wall 30° left

40 km/h wall 30° right

56 km/h ODB left

56 km/h ODB right

32 km/h wall 30° left

32 km/h wall 30° right

...

10 km/h RCAR right

Truck - other LC (50 ms)

E1_F_T_00000 E1_F_T_p0200 E1_F_T_m0200

23 -3 48

20 40 -2

58 56 35

58 56 35

33 1 50

34 50 1

59 52 57

59 57 52

... ... ...

59 57 57

Summe

68

58

149

149

84

85

168

168

...

173

Table 13.9: Classification times of the Wall 0° load cases and Truck load case in the Truck classification algorithm CT [ms]

Load case

56 km/h Wall 0° 50 km/h Wall 0° 40 km/h Wall 0° 32 km/h Wall 0° 27 km/h Wall 0° 30 km/h Truck

-15%

nom.

+15%

18.5 19.5 20.5 22.5 24.5

18.5 19.5 21 22.5 25

18.5 19.5 20.5 22.5 24.5

25

25

25

13.5 Overall Result of a Two-Stage Algorithm

243

Diagram 13.6: Classification of Truck load case for nominal signal values

13.4 Application of First Stage In accordance with the methodology described in Chapter 3, the optimum application threshold can be determined for the Truck and Pole load cases. Ideally, a simple parallel displacement of the original threshold can lead to a solution that triggers both the nominal and amplitude variations of the two load cases in time. Diagram 13.7 shows a threshold increased by 20 CITn values. If the firing times are determined on the basis of this application, then the figure shown in Table 13.10 is obtained. Both load cases meet the requirements on timely triggering of the restraint systems over the entire width of the amplitude variation.

13.5 Overall Result of a Two-Stage Algorithm If the result of the overall algorithm with its threshold switching is considered for the two load cases Truck and Pole, then it can be seen that the firing time requirements can be fully met with the significantly reduced number of

244

Chapter 13 Two-Stage Algorithm to Minimize the Number of Sensors

Diagram 13.7: Threshold adaptation for triggering the Pole and Truck load case with nominal signals and with ±15% amplitude variation Table 13.10: Firing times of Pole and Truck load cases after threshold adjustment FTR [ms]

Firing Time [ms]

Deviation [%]

Firing Time [ms]

Deviation [%]

Firing Time [ms]

Deviation [%]

+15% signal

max.

nominal signal

nom.

35 km/h Pole 30 km/h Truck

-15% signal

min.

Load case

36.9 40.8

41 51

49.2 61.2

39 55.5

-4.9 8.8

38 48

-7.3 -5.9

37.5 47

-8.5 -7.8

Ø +2.0

Ø -6.6

Ø -8.2

13.5 Overall Result of a Two-Stage Algorithm

245

sensors. Table 13.11 shows the resulting firing times of the overall application. Table 13.11: Firing times of all load cases in a two-stage algorithm FTR [ms]

Firing Time [ms]

Deviation [%]

Firing Time [ms]

Deviation [%]

Firing Time [ms]

Deviation [%]

+15% signal

max.

nominal signal

nom.

56 km/h Wall 0° 50 km/h Wall 0° 40 km/h Wall 0° 32 km/h Wall 0° 27 km/h Wall 0° 64 km/h ODB l. 64 km/hODB r.t 40 km/h Wall 30° l. 40 km/h Wall 30° r. 56 km/h ODB l. 56 km/h ODB r. 32 km/h Wall 30° l. 32 km/h Wall 30° r. 40 km/h ODB l. 40 km/h ODB r. 35 km/h Pole 30 km/h Truck

-15% signal

min.

Load case

8 10.4 12.8 13.6 16 25.2 25.2 28.8 28.8 31.5 31.5 35.1 35.1 36.9 36.9 36.9 40.8

10 13 16 17 20 28 28 32 32 35 35 39 39 41 41 41 51

12 15.6 19.2 20.4 24 33.6 33.6 38.4 38.4 42 42 46.8 46.8 49.2 49.2 49.2 61.2

11.5 12.5 15 18 23.5 33 33 35 34 36.5 37.5 43 43 41.5 41.5 39 55.5

15.0 -3.8 -6.3 5.9 17.5 17.9 17.9 9.4 6.3 4.3 7.1 10.3 10.3 1.2 1.2 -4.9 8.8

11 12 15 18 20.5 29 29 29.5 30.5 35.5 35.5 39 39 41.5 42 38 48

10.0 -7.7 -6.3 5.9 2.5 3.6 3.6 -7.8 -4.7 1.4 1.4 0.0 0.0 1.2 2.4 -7.3 -5.9

11 12 14.5 17.5 20.5 27.5 28.5 28.5 28.5 30 30 34 36 40.5 40.5 37.5 47

10.0 -7.7 -9.4 2.9 2.5 -1.8 1.8 -10.9 -10.9 -14.3 -14.3 -12.8 -7.7 -1.2 -1.2 -8.5 -7.8

Ø +6.8

Ø -0.4

Ø -5.4

14 Validation of the Algorithm in Real Crash Tests In the previous chapters, a methodology based on local component loads was presented with the help of which an algorithm could be developed that enables timely triggering for accident scenarios that represent the real-world load case collective. This methodology was developed using simulation data. In order to prove the correct functioning of this new crash algorithm, a functional demonstration is carried out on the basis of real crash tests.

14.1 Experimental Programme A state-of-the-art load case set was presented in Chapter 3. On the basis of this complete set, the proof of function is to be provided on the basis of representative tests. Figure 14.1 describes the load case set in the dimensions crash type and crash speed. Four representative tests were selected from the total load case set. Each load case group is represented once in the test set. In addition, the test speed is varied from slow at 32 km/h to fast at 64 km/h. The tests were all carried out on vehicles that have a comparable front end structure due to their platform similarity. In the test setup, the real acceleration sensors could therefore be mounted on the components planned in the simulation with slight variations in position in the spatial coordinate system.

14.2 Measurement Setup For function validation, sensors 1 to 17 were installed in real crash vehicles in accordance with Chapter 13 and were measured as part of the crash execution. The transition from the simulated sensor measuring points to the sensors © Springer Fachmedien Wiesbaden GmbH, part of Springer Nature 2020 A. Leschke, Algorithm Concept for Crash Detection in Passenger Cars, https://doi.org/10.1007/978-3-658-29392-5_14

248

Chapter 14 Validation of the Algorithm in Real Crash Tests

Figure 14.1: State-of-the-art load case set and selected test support points

actually installed in the vehicle is shown in Figure 14.2 using plane 2 as an example. When using the sensor set, which has been reduced to 17 sensors, three different planes in the vehicle must be fitted with sensors. Figure 14.3 shows an overview of the front end of one of the test vehicles and of the sensors placed in it. The crash tests were carried out according to the barrier type and speed specifications and were filmed with high-speed cameras in various positions for further analysis. Figure 14.4 shows the sequence of the 32 km/h Wall 30° test from two camera positions.

14.3 Test Results During the test, measured values were determined for the 17 sensors installed in the three vehicle planes. The signals were pre-processed and converted into

14.3 Test Results

Figure 14.2: Sensor measuring points in the simulation and sensors in the vehicle

Figure 14.3: Sensor measuring points in the vehicle before the test

249

250

Chapter 14 Validation of the Algorithm in Real Crash Tests

Figure 14.4: 32 km/h Wall 30° vehicle test

14.3 Test Results

251

Diagram 14.1: 32 km/h Wall 30° vehicle test

CITn values using the Matlab tool chain according to the filter chains described in the previous chapters. If these CITn values are imported into the application, then the result shown in Diagram 14.1 is generated as an example for the 32 km/h Wall 30° load case. The upper and lower limits determined from the amplitude variation are shown in grey. The test was to be within these limits. As can be seen, the result of the real vehicle test is within the expected variations. Diagram 14.2 shows the result of the 64 km/h ODB test in the same way, supplemented by the simulation data for nominal signals. Here, too, the test matches the expected behaviour very well. The test is shown in blue, the curve determined from the simulation for nominal signals is dashed in blue. A comparison of the signal characteristics shows that the test is at the lower edge of the test scattering. In Diagram 14.3, the three load cases 50 km/h Wall 0°, 32 km/h Wall 30° and 64 km/h ODB which have been triggered by the initial threshold have been imported into the application. It can be seen that all tests trigger on the section of the application threshold that has been intended for them.

252

Chapter 14 Validation of the Algorithm in Real Crash Tests

Diagram 14.2: 64 km/h ODB vehicle test

Diagram 14.3: Application for 64 km/h ODB, 32 km/h Wall 30° and 50 km/h Wall 0° vehicle test

14.3 Test Results

253

Table 14.1: Firing time results of crash tests FTR [ms]

nom.

max.

Firing Time [ms]

Deviation [%]

50 km/h Wall 0° 64 km/h ODB l. 32 km/h Wall 30° l.

CFT [ms]

min.

Load case

10.4 25.2 35.1

13 28 39

15.6 33.6 46.8

12 31.5 37

-7.7 12.5 -5.1 Ø -0.1

The Crash Firing Times (CFT) shown in Table 14.1 result from the application. All crash tests result in firing times that are within the specified tolerances. The tests have confirmed that the algorithm performs as intended. As described in the previous chapter, the Pole load case must be triggered by shifting the application threshold. The Pole load case must therefore be classified as such in time. Diagram 14.4 shows the classification according to Chapter 13. After 19.5 ms, the Pole test has been classified as a Pole load case, and the overall algorithm can be switched to the second threshold. With the firing threshold shifted on the basis of the classification (Diagram 14.5), the firing time shown in Table 14.2 results for the Pole load case. The Pole load case fired at 43.5 ms and thus within the expected corridor. The overall concept of the two-stage algorithm with threshold switching has led to the desired result in all crash tests. The function of the overall CI algorithm concept is thus confirmed by the tests carried out.

254

Chapter 14 Validation of the Algorithm in Real Crash Tests

Diagram 14.4: Classification of the Pole crash test

Diagram 14.5: Application for Pole load case with threshold adjustment

14.3 Test Results

255

Table 14.2: Firing time result of Pole load case with shifted threshold

min.

nom.

max.

Firing Time [ms]

35 km/h Pole

CFT [ms]

36.9

41

49.2

43.5

Deviation [%]

FTR [ms]

Load case

6.1 Ø +6.1

15 Summary and Qutlook In this chapter, the results of the work are presented in relation to the research questions, and a concluding examination of the results of the work is carried out. Subsequently, an outlook is given on further fields of investigation and new questions.

15.1 Answers to the Research Questions In Section 1.4, four central research questions were posed which should be answered in the context of this work. The central work result was defined as follows in a task description: The overall result of the work is the provision of a validated algorithm concept for the timely firing of restraint systems in a crash, with simultaneous classification according to accident type and accident direction. In this thesis, an algorithm concept based on a local component load was developed and validated. The results generated here serve to answer the research questions. In the following, the work results for each of the formulated research questions are presented. 1. Does the destruction and the resulting load on components in the front end correlate with the type of crash and the speed of the opponent? Chapter 4 showed how the temporal and spatial course of a crash can be transformed into a load wave. It was shown that these load waves are different for different crash types and different for different crash speeds within the same crash type. At sensor signal level, typical component deceleration curves and speed reduction curves of individual components were used to show that local loads provide sufficient information to enable a differentiation with regard to crash type and speed. The first research question has thus been answered in the affirmative. © Springer Fachmedien Wiesbaden GmbH, part of Springer Nature 2020 A. Leschke, Algorithm Concept for Crash Detection in Passenger Cars, https://doi.org/10.1007/978-3-658-29392-5_15

258

Chapter 15 Summary and Qutlook

2. Can the load on the local components in the front end be measured in such a way that a new evaluation variable can be developed from the measurement signals, with the aid of which a timely triggering of restraint systems for different accident types and speeds is possible using an algorithm? In Chapter 7, various evaluation variables such as the CI value or the CITn value were developed with the help of which input variables for an algorithm can be derived from the load waves. On the basis of these evaluation variables, an algorithm concept was developed in Chapter 9, Chapter 10 and Chapter 11, and proof was furnished that the requirements on a crash algorithm that were formulated in Chapter 4 are fulfilled by the new CI algorithm. For all crash load cases contained in the load case set it could be shown that timely triggering of the restraint systems was performed both for load cases with a CITn curve that was based on nominal signals as well as for load cases in which the CITn values were generated from ±15% amplitude variation of the input signals. The second research question is thus answered in the affirmative. 3. Is the evaluation parameter developed in this thesis sufficient to develop advanced algorithms that classify the considered load cases according to the type of load case and its direction? Chapter 12 showed that load cases and hit directions can be classified by a targeted selection of characteristic sensors. All load cases of the considered load case set could be assigned correctly and timely to the different load case groups. In addition, it has been shown that it is possible to distinguish between the left and right hit directions. The third research question is thus answered in the affirmative. 4. Can the described algorithms be built up in a single step on the basis of a single measurand? Chapter 5 presented a classification model for threshold-based algorithms. Within the framework of this model, requirements on the CI algorithm were formulated. The requirements formulated in this model for a single-stage algorithm for a firing decision and on further single-stage algorithms, for various classifications could be fully implemented. It could be shown that the CITn value as an input value is sufficient to fulfil the requirements on the

15.1 Answers to the Research Questions

259

algorithm. As the only limitation compared to the requirement model, it was shown that the Wall 0° load case group is so dominant in its CIT values that only time-limited algorithms were possible in the classification algorithms for all other load case groups. However, despite this limitation, all requirements on the function of the overall algorithm concept could be fulfilled. The fourth research question has been answered in the affirmative, with the exception of the restriction of temporal independence of some classification algorithms. In the course of the preparation of this thesis, further questions were identified and answered. 1. Can the CI algorithm concept be designed to be so robust that, despite the variations in the input variables that are caused by test scattering, timely triggering of the restraint systems is ensured for each load case? In order to design the algorithm robustly, requirements were formulated which can ensure timely triggering of the restraint systems in the case of test scattering that is due to variations in vehicle production. The input signals at sensor level were varied by ±15% and were converted to new CITn values for each load case. Based on this load case variance, the application was optimized both for the main algorithm and for the classification algorithms. It has been shown that the requirement on timely triggering and classification can be met with the load case set extended by the variation. The first new question has thus been answered in the affirmative. 2. Can the CI algorithm be designed so robustly that additional crash types not included in the design load case set also lead to timely triggering of restraint devices? Within the scope of the creation of an application, various new load case types from the future NCAP requirements and known load cases that have been varied with regard to their speed were imported into the algorithm. It could be shown that, for these load cases, the CI algorithm concept results in firing times that fulfil the requirements. The new algorithm concept is very robust, especially since these load cases were subjected to an amplitude variation that also fulfilled all requirements. The second new question has thus been answered in the affirmative.

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3. Can the number of required sensors be reduced significantly if a twostage algorithm concept can be applied? Within the scope of the work, a sensor set of 71 sensors was identified on the basis of which all requirements can be fulfilled. In order to significantly reduce the number of sensors, the requirement on single-stage algorithms was eliminated. If the Pole and Truck load cases are removed from the load case set, then it has been shown that the number of sensors required can be reduced to 17. On the basis of a classification with subsequent threshold adjustment, it could be shown that timely triggering can be implemented for the two load cases mentioned. In this two-stage concept it could also be shown that all requirements on main algorithm and classification algorithms can be fulfilled. The third new question has thus been answered in the affirmative. As an overall result of the work, it can be confirmed that the set task stated below was fulfilled. "The overall result of the work is the provision of a validated algorithm concept for the timely firing of restraint systems in a crash, with simultaneous classification according to accident type and accident direction." The functionality of the algorithm concept could be validated both in the simulation and in real crash tests.

15.2 Outlook In the context of this work, a new algorithm concept was developed, which uses the local load on components as a physical basis. The algorithms developed and the proof of functionality focussed on a front crash application. In order to apply the algorithm concept in a vehicle, the methodology presented should be extended to a side and rear application. In principle, the approaches presented are transferable to further use cases. However, especially with regard to side crash detection, further work is necessary. On the one hand, side collisions demand significantly faster firing times. On the other hand, the limited installation space in the side area of the vehicle results in there being far fewer individual components that can contribute to the CITn value in a side algorithm. These

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deviating framework conditions compared to the frontal crash should be dealt with in order to extend the methodological evidence to all relevant accident types. A further field of action results from the introduction of the CITn value as a new evaluation variable for accident severity. In this work, both the simulation data and the measured variables determined in the real validation tests are based on data from acceleration sensors as they are used in almost all vehicle concepts according to the current state-of-the-art. As was shown, the acceleration data are simplified considerably by a conversion into CI values before they are processed in the new algorithm concept. The one-time crossing of a load limit in the form of a defined velocity reduction is sufficient as an evaluation variable. This simplification allows the use of new sensor concepts, which could be much simpler with regard to their technical setup. In the context of further development of the concept, the development of the new sensors should be investigated further. The new algorithm presented and a sensor concept which is yet to be developed should be combined to effectively transfer the overall potential of the new approach to series development.

Bibliography Achleitner, A. and et. al. (2016). Kapitel 4 Formen und neue Konzepte. In Pischinger, S. and Seiffert, U., editors, Vieweg Handbuch Kraftfahrzeugtechnik, ATZ / MTZ-Fachbuch. Springer Vieweg, Wiesbaden. Ahlers, H. (1997). Multisensorikpraxis. Springer Berlin Heidelberg, Berlin, Heidelberg. Ammoser, H. and Hoppe, M. (2006). Glossar Verkehrswesen und Verkehrswissenschaft: Definitionen und Erläuterungen zu Begriffen des Transport- und Nachrichtenwesens: Diskussionsbeiträge aus dem Institut für Wirtschaft und Verkehr, volume 2/2006. Inst. für Wirtschaft und Verkehr, Dresden. Angermann, A. (2014). Matlab - Simulink - Stateflow: Grundlagen, Toolboxen, Beispiele. Studium. 8. edition. Anselm, D. and Danner, M. (1985). Crash-Reparaturversuche - Eine Entwicklung des Allianz Zentrum für Technik. Maschinen-Schaden, 58(3):120–125. Backhaus, K., Erichson, B., Plinke, W., and Weiber, R. (2016). Multivariate Analysemethoden: Eine anwendungsorientierte Einführung. Springer Gabler, Berlin and Heidelberg, 14. edition. Barényi, I. B. (1952). Das Prinzip des gestaltfesten Fahrerraums. Germany. Patent Npo. 854.157. Basir, O. and Karray, F. (2005). Intelligent Airbag System. Patent No. US 688.921.5B2. Baumann, K. and Grösch, L. (1990). 50 Jahre passive Sicherheit - 30 Jahre Unfallversuche bei Mercedes-Benz. ATZ, (92):242–251. Bell, L. E. (1972). Crash sensing switch. Patent No. US 3.688.063. Benker, H. (2000). Mathematik mit MATLAB: Eine Einführung für Ingenieure und Naturwissenschaftler. Springer-Verlag Berlin Heidelberg. Benker, H. (2010). Ingenieurmathematik kompakt - Problemlösungen mit MATLAB: Einstieg und Nachschlagewerk für Ingenieure und Naturwissenschaftler. Springer-Verlag Berlin Heidelberg. Benz, K. F. (1886). Fahrzeug mit Gasmotorenantrieb. Germany. Patent No. 37.435.

© Springer Fachmedien Wiesbaden GmbH, part of Springer Nature 2020 A. Leschke, Algorithm Concept for Crash Detection in Passenger Cars, https://doi.org/10.1007/978-3-658-29392-5

264

Bibliography

Beucher, O. (2018). Signale und Systeme: Theorie, Simulation, Anwendung: Eine beispielorientierte Einführung mit MATLAB. Springer-Verlag Berlin Heidelberg , Berlin, 3. edition. Blackburn, B. K. and et. al. (1991). Method and Apparatus for Sensing a Vehicle Crash in Real Time Using a Frequency Domain Integration and Summation Algorithm. Patent No. US 5.036.467. Blackburn, B. K. and Gentry, S. B. (1992). Method and Apparatus for Sensing a Vehicle Crash in the Frequency Domain. Patent No. US 5.109.341. Bohlin, N. (1958). Aktiebolaget Volvo: Safety Belt. Patent No. US 3.043.625. Braess, H. (1996). Aktive und passive Sicherheit im Strassenverkehr: Vorschlag einer übergreifenden Gesamtbetrachtung. Zeitschrift für Verkehrssicherheit, (42):50–52. Brede, U. and et. al. (1975). Electronic sensor for triggering safety devices during the crash of vehicles. Patent No. US 3.870.894. Breed, D. (1976). Gas damped vehicular crash sensor with gas being dominant force on sensor. Patent No. US 3.974.350. Bubb, H. and et. al. (2016). Kapitel 6 Aufbau. In Pischinger, S. and Seiffert, U., editors, Vieweg Handbuch Kraftfahrzeugtechnik, ATZ / MTZ-Fachbuch. Springer Vieweg, Wiesbaden. Burg, H. and Moser, A. (2017). Handbuch Verkehrsunfallrekonstruktion: Unfallaufnahme, Fahrdynamik, Simulation. ATZ/MTZ-Fachbuch. 3. aufl. 2017 edition. carhs.training GmbH (2017). Safety Companion 2017. carhs.training, Alzenau. Cashler, R. J. and Kelley, J. P. (1995). SIR Deployment Method Based on Occupant Displacement and Crash Severity. Patent No. US 5.430.649. Chan, C.-Y. (2000). Fundamentals of crash sensing in automotive air bag systems. SAE Society Of Automotive Engineering, Warrendale. Cleff, T. (2015). Regressionsanalyse. In Cleff, T., editor, Deskriptive Statistik und Explorative Datenanalyse, pages 135–170. Gabler Verlag, Wiesbaden. Diller, R. (1991a). Apparatus and method employing multiple crash evaluation algorithms and evaluation expertise for actuating restraint system in an passenger vehicle. Patent No. US 5.040.118. Diller, R. (1991b). Electronic Sensing of Automobile Crashes for Airbag Deployment. In SAE, editor, SAE technical paper series, volume 910276. Society of Automotive Engineers, Warrendale. Eigler, J. and Weber, R. (1993). Control Unit for a Passenger Restraint System and / or Passenger Protection System for Vehicles. Patent No. US 5.229.943.

Bibliography

265

Engelmann, S., Neubohn, A., and Meywerk, M. (2012). Simulation von fahrwerkdominierten Misuse-Lastfällen zur Unterstützung der virtuellen Crashsensorik. In Berechnung, Simulation und Erprobung im Fahrzeugbau 2012, volume 2169 of VDI-Berichte, pages 853–864. VDI-Verl., Düsseldorf. ESI Group (2011). Virtual Performance Solution 2011 - Solver Reference Manual. Paris. EuroNCAP (2017). European new car assement programme - offset deformable barrier frontal impact testing protocol version 7.1.2. https://cdn.euroncap.com/media/ 32286/euro-ncap-frontal-odb-test-protocol-v712.pdf. Europäische Union (2011). Weißbuch zum Verkehr: Fahrplan zu einem einheitlichen europäischen Verkehrsraum; hin zu einem wettbewerbsorientierten und ressourcenschonenden Verkehrssystem. Amt f. Veröff. d. Europ. Union, Luxemburg. Fahrmeir, L., Heumann, C., Künstler, R., Pigeot, I., and Tutz, G., editors (2016). Statistik. Springer-Lehrbuch. Springer Berlin Heidelberg, Berlin, Heidelberg. Fails, A. and Minton, R., editors (2001). Comparison of EuroNCAP Assessments with injury causation in accidents. SAE Technical Paper. Feser, M., Wieand, C., Schmidt, C., and Brandmeier, T. (2004). Crash Impact Sound Sensing (CISS) - Higher Performance for Lower Costs. Airbag 2004, Karlsruhe. Gioutsos, T. (1992). A Predictive Based Algorithm for Actuation of an Airbag. In SAE, editor, SAE technical paper series, volume 920479. Society of Automotive Engineers, Warrendale. Gonter, M. and Leschke, A. (2016). Kapitel 9 Fahrzeugsicherheit. In Pischinger, S. and Seiffert, U., editors, Vieweg Handbuch Kraftfahrzeugtechnik, ATZ / MTZ-Fachbuch. Springer Vieweg, Wiesbaden. Hauke, J. and Kossowski, T. (2011). Comparison of Values of Pearson’s and Spearman’s Correlation Coefficients on the Same Sets of Data. Quaestiones Geographicae, 30(2):87– 93. Henne, M., Groesch, L., Hafner, S., and Baumann, T. (2002). Method and Device for Forming a Deployment Criterion for a Restraint System. Patent No. US 6.353.753. Hollowell, William T. et. al. (2018). Review of Potential Test Procedures for FMVSS No. 208. National Highway Traffic Safety Administration. Insurance Institute for Highway Safety (2017). Moderate Overlap Frontal Crashworthiness Evaluation Crash Test Protocol (Version XVIII). https: //www.iihs.org/media/f70ff6eb-d7a1-4b60-a82f-e4e8e0be7323/NXIOUQ/ Ratings/Protocols/current/test_protocol_high.pdf. International Traffic Safety Data and Analysis Group (IRTAD) (2006). International Road Traffic and Accident Database. https://www.itf-oecd.org/IRTAD.

266

Bibliography

ISO (2011). ISO 26262, Road vehicles – Functional safety. Jace, A. (1992). Power-rate crash sensing method for safety device actuation. In SAE, editor, SAE technical paper series, volume 920478. Society of Automotive Engineers, Warrendale. Justen, R. and Baumann, K. (1997). Pre-Crash-Erkennung, ein neuer Weg in der PkwSicherheit. In Verein Deutscher Ingenieure, editor, VDI Gesellschaft Fahrzeug- und Verkehrssystemtechnik, volume 1471, pages 361–379. VDI-Verl., Düsseldorf. Klanner, W. (2004). Unfallverletzungen in Fahrzeugen mit Airbag, volume Heft F53 of Berichte der Bundesanstalt für Strassenwesen. Fahrzeugtechnik. Bundesanstalt für Strassenwesen, Bergisch Gladbach, Germany. Kozyref, M. (2004). Sicherheitsanordnung. Patent No. DE 1.958.177.2B4. Kramer, F. (2009). Passive Sicherheit von Kraftfahrzeugen. ATZ / MTZ-Fachbuch. Springer Fachmedien, Wiesbaden. Kramer, F., editor (2013). Integrale Sicherheit von Kraftfahrzeugen: Biomechanik - Simulation - Sicherheit im Entwicklungsprozess ; mit 55 Tabellen. ATZ/MTZ-Fachbuch. Springer Vieweg, Wiesbaden, 4. edition. Kuckartz, U., Rädiker, S., and Ebert, T. (2013). Statistik: Eine verständliche Einführung. Springer Berlin-Heidelberg, 2. edition. Lehmann, D., editor (1994). Aktuelle und zukünftige Vorschriften zur Bewertung der passiven Sicherheit. 2. International Symposium on Sophistcated Car Occupant Safety Systems. Karlsruhe. Leschke, A. and Weinert, F. (2017). Qualität und Sicherheit im Verkehr, Vorlesung im Bachelorstudiengang Verkehrssystemtechnik der TH Wildau. Leveau, G.-D. (1903). Bretelles protectrices pour voitures automobiles et autres. France. Patent No. 331.926 . Lie, A. and Tingvall, C. (2002). How Do Euro NCAP Results Correlate with Real-Life Injury Risks? A Paired Comparison Study of Car-to-Car Crashes. Traffic Injury Prevention, 3(4):288–293. Linderer, W. (1951). Einrichtung zum Schutze von in Fahrzeugen befindlichen Personen gegen Verletzungen bei Zusammenstößen. Patent No. DE 896.312. Mattes, B. and et. al. (1991). Method for Controlling the Release of Passenger Restraint Systems. Patent No. US 5.014.810. McFarlane, A. (2010). How the UKs first fatal car accident unfolded . BBC News Magazine, https://www.bbc.com/news/magazine-10987606.

Bibliography

267

Meyer, M. (2017). Signalverarbeitung: Analoge und digitale Signale, Systeme und Filter. Lehrbuch. Springer Vieweg, Wiesbaden, 8. edition. Milligan, G. W. and Cooper, M. C. (1985). An examination of procedures for determining the number of clusters in a data set. Psychometrika, 50(2):159–179. Mojena, R. (1977). Hierarchical grouping methods and stopping rules: an evaluation. Computer Journal, 20:359–363. Moritz, R. (2000). Pre-crash Sensing - Its Functional Evolution Based on a Platform Radar Sensor. SAE Transactions, 109:437–446. Murray, C. J. L., editor (1996). The global burden of disease: A comprehensive assessment of mortality and disability from diseases, injuries, and risk factors in 1990 and projected to 2020 ; summary, volume 1 of Global burden of disease and injury series. Harvard School of Public Health, Cambridge. Olsson, J. (1994). An accelerometer arrangement. Patent No. US 534.341. Pearson, K. (1920). Notes on the History of Correlation. Biometrika, 13(1):25. Peden, M. (2004). World report on road traffic injury prevention: information kit. World Health Organization, Geneva, Switzerland. RCAR (2010). Bumper test. Issue 2.0. http://www.rcar.org/Papers/Procedures/ RCAR%20Bumper%20Test%20Procedure%20issue%202_1%20revFeb18.pdf. Reif, K. (2012). Bosch Grundlagen Fahrzeug- und Motorentechnik: Konventioneller Antrieb, Hybridantriebe, Bremsen, Elektronik. Bosch Fachinformation Automobil. Vieweg+Teubner Verlag / Springer Fachmedien Wiesbaden GmbH Wiesbaden, Wiesbaden, 1. edition. Reif, K. (2014). Passive Sicherheit. In Reif, K., editor, Automobilelektronik, pages 283–299. Springer Fachmedien Wiesbaden, Wiesbaden. Reneau, D. R. (1993). Sock sensor with magnetically operated reed switch. Patent No. US 4.980.526. Rinn, J. (1991). Der Luftsack hat Entfaltungspotential. Automobil Industrie, 1:44–46. Schmidt, N. and Acht, J. (2004). Drucksensor. Patent No. DE 101.631.64A. Schuh, K. (2011). Band 15 - Der Sicherheitsgurt - Lebensretter Nr. 1. In DVR Schriftenreihe. Deutscher Verkehrssicherheitsrat, Wiesbaden. Schweizer, W. (2013). MATLAB kompakt: [MATLAB and Simulink® examples]. 5. edition. Seiffert, U. (1992). Fahrzeugsicherheit: Personenwagen. VDI-Verl., Düsseldorf. Seiffert, U. (1998). Die Automobiltechnik nach der Jahrtausendwende. Euroforum München.

268

Bibliography

Seiffert, U., editor (2004). Quantensprung für die Fahrzeugsicherheit, volume 106 of Automobiltechnische Zeitschrift. Franckh Kosmos Verlags GmbH und Co. Siela, J. and Recke, L., editors (1990). Simulation des Crashverhaltens eines PKW-Vorbaus in der Vorentwicklungsphase mit einem vereinfachten FEM-Modell, volume 818 of VDIBerichte. Spearman, C. (1904). The Proof and Measurement of Association between Two Things. The American Journal of Psychology, 15(1):72. Statistisches Bundesamt (2016). Verkehr 2016: Verkehr im Überblick. Statistisches Bundesamt. Stieniczka, N. (2006). Das narrensichere Auto: Die Entwicklung passiver Sicherheitstechnik in der Bundesrepublik Deutschland. Edition Universität. Wiss. Buchges, Darmstadt. Tohbaru, S. (1993). Collision Determining Circuit Having a Starting Signal Generating Circuit. Patent No. US 5.256.904. Urban, D. and Mayerl, J. (2018). Angewandte Regressionsanalyse: Theorie, Technik und Praxis. Studienskripten zur Soziologie. 5. edition. Volkswagen Aktiengesellschaft Academy Sales & Service (2009). Selbststudienprogramm Karosserie-Grundlagen. Wolfsburg. Volkswagen Aktiengesellschaft Academy Sales & Service (2013a). Selbststudienprogramm Der Golf 2013. Wolfsburg. Volkswagen Aktiengesellschaft Academy Sales & Service (2013b). Selbststudienprogramm Der Golf 2013 Karosserie und Insassenschutz. Wolfsburg. Volkswagen Aktiengesellschaft Academy Sales & Service (2013c). Selbststudienprogramm Insassenschutz - Passive Systeme Konstruktion und Funktion. Wolfsburg. Volz, K., Dirschmid, F., and Duddeck, F. (2006). Optimierung der Crasheigenschaften von Rohkarosserien in der frühen Phase der Produktentwicklung. In Berechnung und Simulation im Fahrzeugbau, volume 1967, CD-ROM of VDI-Berichte, pages 199–216. VDI-Verl., Düsseldorf. Watanabe, K. and Umerawa, Y. (1993). Optimal Triggering of an Airbag. In International Congress & Exposition, SAE Technical Paper Series. SAE International, Warrendale. Watanabe, K. and Umezawa, Y. (1993). Optimal Timing to Trigger an Airbag. In SAE, editor, SAE technical paper series, volume 930242. Society of Automotive Engineers, Warrendale. Watanabe, K., Umezawa, Y., and Abe, K. (1994). Advanced passive safety system via prediction and sensor fusion. In VNIS’94 - 1994 Vehicle Navigation and Information Systems Conference, pages 435–440.

Bibliography

269

Werner, M. (2008). Signale und Systeme: Lehr- und Arbeitsbuch mit MATLAB-Übungen und Lösungen. Studium. Vieweg + Teubner, Wiesbaden, 3. edition. Wilfert, K. (1973). Entwicklungsmöglichkeiten im Automobilbau. ATZ, pages 273–278. Winner, H., Hakuli, S., and Lotz, F. (2015). Handbuch Fahrerassistenzsysteme: Grundlagen, Komponenten und Systeme für aktive Sicherheit und Komfort. ATZ/MTZ-Fachbuch. 3. edition. World Health Organization (2015). Global status report on road safety 2015. World Health Organization, Geneva, Switzerland. Zander, A. G. (2003). Alternative Sensierungskonzepte zur Seitencrash-Erkennung. Fortschritt-Berichte VDI : Reihe 12, Verkehrstechnik, Fahrzeugtechnik.

Accompanying Publications Leschke, A. and Weinert, F. (2017). Neuer Sensoransatz zur Crasherkennung im PKW. In Kubica, S., Ringshausen, H., Reiff-Stephan, J., and Schlingelhof, M., editors, 2. Automobil Symposium Wildau: Tagungsband Technische Hochschule Wildau 2017, pages 47 – 56. Leschke, A., Weinert, F., and Bonaiuto, V. (2018). Innovative Approach to Crash Detection in Passenger Cars Enabled by Passive RFID Tags. International Journal of Automotive Science and Technology, 2(2):1–8. Leschke, A., Weinert, F., Obermeier, M., Kubica, S., and Bonaiuto, V. (2020). Method for Classification of Frontal Collision Events in Passenger Cars Based on Measurement of Local Component-Specific Decelerations. The International Journal of Automotive Technology, 21(1).

In order to meet the requirements of the PhD program regulations, the papers [Leschke et al., 2020], [Leschke et al., 2018] and [Leschke and Weinert, 2017] contained in this list have been pre-published. The papers are therefore not explicitly cited in the dissertation. .

© Springer Fachmedien Wiesbaden GmbH, part of Springer Nature 2020 A. Leschke, Algorithm Concept for Crash Detection in Passenger Cars, https://doi.org/10.1007/978-3-658-29392-5