Deep Learning and Its Applications for Vehicle Networks 1032041374, 9781032041377
Deep Learning (DL) is an effective approach for AI-based vehicular networks and can deliver a powerful set of tools for
396 109 50MB
English Pages 342 [357] Year 2023
Cover
Half Title
Title Page
Copyright Page
Table of Contents
Preface
About the Editors
List of Contributors
Part I: Deep learning for vehicle safety and security
Chapter 1: Deep learning for vehicle safety
1.1 Introduction
1.2 Deep learning for internal vehicle monitoring
1.2.1 Camera-based system
1.2.2 Wearable sensor-based system
1.2.3 Driver behavior monitoring
1.3 Deep learning for surrounding environment perception
1.3.1 Road detection
1.3.2 Vehicle surrounding environment detection
1.3.3 Object detection in challenging environments
1.4 Deep learning for traffic management
1.4.1 Traffic flow modelling
1.4.2 Vehicle and infrastructure communications
1.5 Deep learning-based route planning and navigation
1.5.1 Route planning for travellers
1.5.2 Route planning for food transportation
1.5.3 Dynamic routing with unknown map
1.6 Conclusions
References
Chapter 2: Deep learning for driver drowsiness classification for a safe vehicle application
2.1 Introduction
2.1.1 Importance of drowsiness detection
2.1.2 Application in future automated vehicles
2.2 Driver drowsiness detection methods
2.2.1 Subjective measures
2.2.2 Objective measures
2.2.2.1 Input sources for driver drowsiness detection
2.2.3 Deep learning methods
2.2.3.1 Deep learning methods applied to the biosignals
2.3 Comparison of methods
2.4 Summary and outlook
Notes
References
Chapter 3: A deep learning perspective on Connected Automated Vehicle (CAV) cybersecurity and threat intelligence
3.1 Introduction
3.2 CAV technological enablers: automation and connectivity
3.3 CAV threat landscape and threat intelligence
3.3.1 In-vehicle (low-level sensor) cyber vulnerabilities
3.3.2 Vehicle control modules
3.3.3 Security analysis of CAV threats
3.3.4 Attack surfaces
3.3.5 Organizational risks to CAV ecosystem
3.4 CAV threat mitigation: anomaly detection and classification with deep learning
3.5 Frontiers in deep learning (advancement and future)
3.5.1 Meta-learning
3.5.2 Federated learning
3.6 End-to-end deep CNN-LSTM architecture for CAV cyberattack detection
3.6.1 Performance analysis
3.6.1.1 Dataset
3.6.1.2 Evaluation metrics
3.6.2 Results and discussions
3.7 Conclusion
References
Part II: Deep learning for vehicle communications
Chapter 4: Deep learning for UAV network optimization
4.1 Introduction
4.2 Key categories for UAV networking throughput enhancement
4.3 Routing enhancement for UAV networking throughput
4.3.1 Position-based routing
4.3.1.1 Single path-based
4.3.1.2 Multiple path-based
4.3.2 Topology-based routing
4.3.2.1 Proactive
4.3.2.2 Reactive
4.3.2.3 Hybrid
4.3.3 Swarm-based routing
4.3.4 DL-enabled routing for UAV networking
4.4 UAV networking construction
4.4.1 UAV swarm networking construction
4.4.2 DL-enabled UAV swarm networking enhancement
4.5 DL-enabled UAV networking throughput
4.5.1 DL-enabled allocation for throughput enhancement
4.5.2 DL-enabled scheduling for throughput enhancement
4.5.2.1 Scheduling for UAV networking
4.5.2.2 DL-enabled scheduling for UAV networking
4.6 Conclusions
References
Chapter 5: State-of-the-art in PHY layer deep learning for future wireless communication systems and networks
5.1 Introduction
5.1.1 Related survey papers
5.1.2 Summary of this chapter
5.2 Data-driven ML methods for transceiver optimization
5.2.1 Data-driven approach for end-to-end transceiver optimization
5.2.2 Model-aided data-driven methods for modular transceiver optimization
5.3 Deep learning for symbol detection
5.3.1 Incorporating expert knowledge into autoencoders
5.3.2 Implementing NNs at the receiver
5.3.3 Sequential detectors using ML
5.4 Channel estimation using ML
5.5 Channel prediction in frequency- and time-domain using ML
5.6 Channel coding using AI/ML
5.7 Intelligent link adaptation
5.8 Intelligent radios
5.8.1 Intelligent spectrum sensing
5.8.2 Automatic signal recognition using CNNs
5.8.3 Intelligent radio environment
5.9 ML for system-level performance evaluation of wireless networks
5.10 Conclusions
Notes
References
Chapter 6: Deep learning-based index modulation systems for vehicle communications
6.1 Introduction
6.2 V2V and V2I Communications
6.3 Deep learning-based index modulation systems
6.3.1 Multicarrier-based index modulation systems
6.3.1.1 Transmitter of system model with OFDM-IM
6.3.1.2 Traditional detection scheme
6.3.1.3 Deep learning-based detector
6.3.1.3.1 Deep learning model
6.3.1.3.2 Training procedure
6.3.1.3.3 Online deployment
6.3.2 Single-carrier based index modulation systems
6.3.2.1 Transmitter of system model with CIM-SS
6.3.2.2 Conventional detection scheme
6.3.2.3 Deep learning-based detection
6.3.3 Multi-input multi-output based index modulation systems
6.3.3.1 System model and related works
6.3.3.2 Deep learning-based SMTs
6.3.3.3 Performance analysis on energy efficiency of vehicle communications with IM
6.4 Conclusions
References
Chapter 7: Deep reinforcement learning applications in connected-automated transportation systems
7.1 Introduction
7.1.1 Chapter organization
7.2 Deep reinforcement learning: theory and background
7.2.1 (Deep) reinforcement learning: a brief history
7.2.2 Classical reinforcement learning
7.2.2.1 Value-based RL
7.2.2.2 Policy-based RL
7.2.3 Deep reinforcement learning
7.2.3.1 Deep Q-networks
7.2.3.2 Deep policy networks
7.2.3.3 Deep actor-critic networks
7.2.4 Formulating (deep) reinforcement learning for CAV application
7.3 Data environment in CAV networks
7.3.1 Benefits
7.3.2 Data generated by AVs
7.4 Deep reinforcement learning applications: connected vehicles
7.4.1 Lane changing and assistance
7.4.2 Traffic signal control
7.4.3 Traffic flow optimization
7.4.4 Rail and maritime transportation
7.4.5 Data communications, computing, and networking
7.4.6 DRL applications for cybersecurity
7.5 Deep reinforcement learning applications: automated driving systems
7.5.1 Motion planning
7.5.2 Lateral control
7.5.3 Safety
7.6 Challenges and future directions
7.6.1 Transferability to real-world applications
7.6.2 Representation of traffic environment
7.6.3 Formulating reward functions
7.6.4 Multi-agent DRL in CAV environment
7.6.5 Partial state observability
References
Part III: Deep learning for vehicle control
Chapter 8: Vehicle emission control on road with temporal traffic information using deep reinforcement learning
8.1 Introduction
8.2 Related work
8.3 Overview
8.3.1 Preliminary
8.3.2 Traffic data insight
8.3.3 Problem formulation
8.4 Methodology
8.4.1 Framework
8.4.2 EFRL model
8.5 Experiments
8.5.1 Data and setup
8.5.2 Baselines and metric
8.5.3 Results
8.6 Conclusion
References
Chapter 9: Load prediction of an electric vehicle charging pile
9.1 Introduction
9.2 Charging load characteristic analysis of electric vehicles
9.3 Quantile regression model of dilated causal convolutional
9.3.1 Dilated causal convolutional
9.3.2 Kernel density estimation
9.3.3 Dilated causal convolutional quantile regression
9.3.4 Model evaluation index
9.3.5 Example simulation based on python
9.4 Spatio-temporal dynamic load prediction of charging pile load based on deep learning
9.4.1 Spatio-temporal dynamic load prediction of the charging pile
9.4.2 Spatio-temporal dynamic load matrix construction
9.4.3 Spatio-temporal convolutional networks model
9.4.4 Spatio-temporal dynamic load forecasting based on dilated causal convolution-2D
9.4.5 Spatio-temporal dynamic load forecasting based on Spatio-temporal neural network
9.4.6 Example simulation based on python
9.5 Conclusions
References
Chapter 10: Deep learning for autonomous vehicles: A vision-based approach to self-adapted robust control
10.1 Introduction
10.2 References selection via deep learning image processing
10.2.1 CNN analytic outcomes as control references
10.2.2 Experimental data
10.2.3 Multi-objective evaluation
10.2.4 Control state variable
10.3 Robust control design
10.3.1 System identification
10.3.2 Robust Linear Quadratic Regulator (RLQR)
10.3.3 H∞ Controller
10.4 Case study for hybrid controller
10.4.1 Simulation environment and problem objective
10.4.2 Machine learning design
10.4.2.1 Input and output
10.4.2.2 Reward function
10.4.2.3 Neural network design
10.4.2.4 Simulation technical details
10.4.3 Hybrid control design
10.4.3.1 Control reference selection
10.4.3.2 System identification and nominal LQR
10.4.3.3 Uncertainty estimation via evolutionary search
10.4.4 Performance evaluation
10.5 Conclusions
Note
References
Part IV: DL for information management
Chapter 11: A natural language processing-based approach for automating IoT search
11.1 Introduction
11.2 IoT search engine
11.2.1 Architecture
11.2.2 Key components
11.2.3 Research challenges
11.3 NLP-based query processing
11.3.1 Design rationale
11.3.2 Basic components of NLP
11.3.3 NLP tools
11.3.4 Comparison of NLTK and spaCy
11.4 The ACQUISE approach
11.4.1 Baseline strategy
11.4.2 Enhanced static strategy
11.4.3 Enhanced dynamic strategy
11.5 Performance evaluation
11.5.1 Methodology
11.5.2 Results
11.6 Discussion
11.6.1 Machine learning
11.6.2 Protocols and algorithms
11.6.3 Security and privacy
11.7 Related work
11.8 Final remarks
Acknowledgement
References
Chapter 12: Toward incentive-compatible vehicular crowdsensing: A reinforcement learning-based approach
12.1 Introduction
12.2 Edge-assisted vehicular crowdsensing
12.2.1 Architecture design
12.2.2 Workflow
12.3 Incentive mechanism for vehicle recruitment
12.3.1 Stackelberg game
12.3.2 Strategy of the SSP
12.3.3 Strategies of vehicles
12.4 Case study
12.5 Conclusions
Appendix A
References
Chapter 13: Sub-signal detection from noisy complex signals using deep learning and mathematical morphology
13.1 Introduction
13.2 LSTM-RNN and mathematical morphology-based algorithm to detect sub-signals from noisy complex signals
13.2.1 Data preparation and pre-processing
13.2.2 LSTM-RNN local sub-signal learning
13.2.3 Mathematical morphological global sub-signal detection
13.3 Experimental results
13.4 Conclusion
Acknowledgment
Notes
References
Part V: Miscellaneous
Chapter 14: The basics of deep learning algorithms and their effect on driving behavior and vehicle communications
14.1 Introduction
14.2 Basics of deep learning algorithms and supervised learning
14.2.1 Linear regression and logistic regression
14.2.2 Artificial Neural Networks
14.2.3 Convolutional Neural Networks
14.2.4 Recurrent Neural Networks
14.2.5 Deep learning architectures
14.3 Deep unsupervised and semi-supervised learning
14.3.1 Restricted Boltzmann Machines and deep belief nets
14.3.2 Autoencoders & variational autoencoders
14.3.3 Generative adversarial networks
14.3.4 Transformers
14.4 Hyperparameters, pre-processing and optimization
14.4.1 Data augmentation and transfer learning
14.4.2 Weight initialization, activation functions and optimizers
14.4.3 Training time, pre-processing and architectural refinements
14.5 Applications of deep learning in driving behavior analysis and vehicle communication
14.6 Conclusions
References
Chapter 15: Integrated simulation of deep learning, computer vision and physical layer of UAV and ground vehicle networks
15.1 Introduction
15.2 Applications that can benefit from CAVIAR simulations
15.2.1 Simulation of UAV-enabled AI/ML
15.2.2 Beam-selection for V2I
15.3 Multi-domain integrated simulators
15.3.1 Wireless channel generation with Raymobtime
15.3.2 Caviar simulations
15.4 Simulations results
15.4.1 Beam selection for V2I with lidar as input
15.4.2 In-loop CAVIAR simulation of a computer vision application
15.4.3 Impact of 3D model accuracy on wireless channels
15.5 Conclusions
Acknowledgments
Notes
References
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Deep Learning and Its Applications for Vehicle Networks
Deep Learning (DL) is an effective approach for AI-based vehicular networks and can deliver a powerful set of tools for such vehicular network dynamics. In various domains of vehicular networks, DL can be used for learning-based channel estimation, traffic flow prediction, vehicle trajectory prediction, locationprediction-based scheduling and routing, intelligent network congestion control mechanisms, smart load balancing and vertical handoff control, intelligent network security strategies, virtual smart and efficient resource allocation and intelligent distributed resource allocation methods. This book is based on the work of world-famous experts on the application of DL to vehicle networks. It consists of the following five parts: (I) DL for vehicle safety and security: This part covers the use of DL algorithms for vehicle safety or security. (II) DL for effective vehicle communications: Vehicle networks consist of vehicle-to-vehicle and vehicle-to-roadside communications. This part covers how intelligent vehicle networks require a flexible selection of the best path across all vehicles, adaptive sending rate control based on bandwidth availability, and timely data downloads from a roadside base-station. (III) DL for vehicle control: The myriad operations that require intelligent control for each individual vehicle are discussed in this part. This also includes emission control, which is based on the road traffic situation, the charging pile load predicted through DL and vehicle speed adjustments based on camera-captured image analysis. (IV) DL for information management: This part covers some intelligent information collection and understanding. We can use DL for energy-saving vehicle trajectory control based on the road traffic situation and given destination information; we can also use natural language processing based on the DL algorithm for automatic Internet of Things (IoT) search during driving. (V) Other applications: This part introduces the use of DL models for other vehicle controls. Autonomous vehicles are becoming increasingly popular in society. DL and its variants will play greater roles in cognitive vehicle communications and control. Other machine learning models, such as deep reinforcement learning, will also facilitate the understanding and adjustment of intelligent vehicle behavior. This book will become a valuable reference for your understanding of this critical field.
Deep Learning and Its Applications for Vehicle Networks
Edited by
Fei Hu and Iftikhar Rasheed
Cover Image: Shutterstock First edition published 2023 by CRC Press 6000 Broken Sound Parkway NW, Suite 300, Boca Raton, FL 33487-2742 and by CRC Press 4 Park Square, Milton Park, Abingdon, Oxon, OX14 4RN CRC Press is an imprint of Taylor & Francis Group, LLC © 2023 selection and editorial matter, Fei Hu and Iftikhar Rasheed; individual chapters, the contributors Reasonable efforts have been made to publish reliable data and information, but the author and publisher cannot assume responsibility for the validity of all materials or the consequences of their use. The authors and publishers have attempted to trace the copyright holders of all material reproduced in this publication and apologize to copyright holders if permission to publish in this form has not been obtained. If any copyright material has not been acknowledged please write and let us know so we may rectify in any future reprint. Except as permitted under U.S. Copyright Law, no part of this book may be reprinted, reproduced, transmitted, or utilized in any form by any electronic, mechanical, or other means, now known or hereafter invented, including photocopying, microfilming, and recording, or in any information storage or retrieval system, without written permission from the publishers. For permission to photocopy or use material electronically from this work, access www.copyright.com or contact the Copyright Clearance Center, Inc. (CCC), 222 Rosewood Drive, Danvers, MA 01923, 978-750-8400. For works that are not available on CCC please contact [email protected] Trademark notice: Product or corporate names may be trademarks or registered trademarks and are used only for identification and explanation without intent to infringe. Library of Congress Cataloging‑in‑Publication Data Names: Hu, Fei, 1972- editor. | Rasheed, Iftikhar, editor. Title: Deep learning and its applications for vehicle networks / edited by Fei Hu and Iftikhar Rasheed. Description: First edition. | Boca Raton : CRC Press, [2023] | Includes bibliographical references and index. Identifiers: LCCN 2022054949 (print) | LCCN 2022054950 (ebook) | ISBN 9781032041377 (hbk) | ISBN 9781032041384 (pbk) | ISBN 9781003190691 (ebk) Subjects: LCSH: Vehicular ad hoc networks (Computer networks) | Deep learning (Machine learning) Classification: LCC TE228.37 .D44 2023 (print) | LCC TE228.37 (ebook) | DDC 006.3/1--dc23/eng/20230105 LC record available at https://lccn.loc.gov/2022054949 LC ebook record available at https://lccn.loc.gov/2022054950 ISBN: 978-1-032-04137-7 (hbk) ISBN: 978-1-032-04138-4 (pbk) ISBN: 978-1-003-19069-1 (ebk) DOI: 10.1201/9781003190691 Typeset in Times by SPi Technologies India Pvt Ltd (Straive)
Contents Preface About the Editors List of Contributors
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PART I Deep learning for vehicle safety and security 1 1 Deep learning for vehicle safety Raiyan Talkhani, Tao Huang, Shushi Gu, Zhaoxia Guo, Guanglin Zhang and Wei Xiang 2 Deep learning for driver drowsiness classification for a safe vehicle application Sadegh Arefnezhad and Arno Eichberger 3 A deep learning perspective on Connected Automated Vehicle (CAV) cybersecurity and threat intelligence Manoj Basnet and Mohd Hasan Ali
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PART II Deep learning for vehicle communications 57 4 Deep learning for UAV network optimization Jian Wang, Yongxin Liu, Shuteng Niu and Houbing Song 5 State-of-the-art in PHY layer deep learning for future wireless communication systems and networks Konstantinos Koufos, Karim El Haloui, Cong Zhou, Valerio Frascolla and Mehrdad Dianati 6 Deep learning-based index modulation systems for vehicle communications Junfeng Wang, Yue Cui, Zeyad A. H. Qasem, Haixin Sun, Guangjie Han and Mohsen Guizani 7 Deep reinforcement learning applications in connected-automated transportation systems H. M. Abdul Aziz and Sanjoy Das
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PART III Deep learning for vehicle control 165 8 Vehicle emission control on road with temporal traffic information using deep reinforcement learning Zhenyi Xu, Yang Cao, Yu Kang and Zhenyi Zhao 9 Load prediction of an electric vehicle charging pile Peng Shurong, Peng Jiayi, Yang Yunhao and Li Bin 10 Deep learning for autonomous vehicles: a vision-based approach to selfadapted robust control Gustavo A. Prudencio de Morais, Lucas Barbosa Marcos, José Nuno A. D. Bueno, Marco Henrique Terra and Valdir Grassi Junior
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PART IV DL for information management 235 11 A natural language processing-based approach for automating IoT search Cheng Qian, William Grant Hatcher, Weichao Gao, Erik Balsch, Chao Lu and Wei Yu
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12 Towards incentive-compatible vehicular crowdsensing: a reinforcement learning-based approach Xinxin Yang and Bo Gu
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13 Sub-signal detection from noisy complex signals using deep learning and mathematical morphology Jie Wei, Hamilton Clouse and Ashley Diehl
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PART V Miscellaneous 293 14 The basics of deep learning algorithms and their effect on driving behavior and vehicle communications Abdennour Najmeddine, Ouni Tarek and Ben Amor Nader 15 Integrated simulation of deep learning, computer vision and physical layer of UAV and ground vehicle networks Aldebaro Klautau, Ilan Correa, Felipe Bastos, Ingrid Nascimento, João Borges, Ailton Oliveira, Pedro Batista and Silvia Lins
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Preface The term Deep Learning (DL) describes the set of machine learning algorithms that applies large neural networks with recurrent or convolutional features. In other words, these are Deep Neural Networks (DNNs) for feature generation, learning, classification, and prediction. Currently, DL has been widely utilized by many online services provided by Amazon, Google, and Facebook. DL has a wide range of applications in the domain of the automotive industry, from computer vision processing for autonomous vehicles to intelligent vehicular networks for providing high data rates. The forthcoming intelligent vehicles will be equipped with a broad range of sensors and actuators, for example, the engine controller, radar, light detection and ranging (LIDAR), and cameras, will enable a vehicle to have knowledge about its surroundings. The vehicles will also incorporate stronger computing capabilities with larger on-board storage devices. This transformation would mean that a traditional vehicular network requires smart processing abilities. DL will be an effective approach for AI-based vehicular networks and can deliver a powerful set of tools for such vehicular network dynamics. In various domains of vehicular networks, DL can be used for learning-based channel estimation, traffic flow prediction, vehicle trajectory prediction, location-prediction-based scheduling and routing, intelligent network congestion control mechanism, smart load balancing and vertical handoff control, intelligent network security strategies, virtual smart & efficient resource allocation and intelligent distributed resource allocation methods. This book is a valuable reference or textbook for college students and researchers who want to understand the intelligent vehicle communication systems. It can also be used by industry engineers and technicians for hardware/software design purpose. This book is based on the work from world-famous experts on the application of DL for vehicle networks. It consists of the following five parts: Part I. DL for vehicle safety and security: In this part, we have a few chapters to cover the use of DL algorithms for vehicle safety or security. Safety aims to overcome the natural faults to ensure that the autonomous vehicle can correctly recognize the images captured by its camera and adopt the proper actions. For example, a vehicle can realize the road obstacle ahead and apply the brake in a timely manner. Security aims to overcome intentional attacks from adversaries. For example, one may insert falsified sensing signals to the vehicle’s RF communication channels to mislead the vehicles; or it pollutes the imaging data to generate wrong DL results. In this part, we will explain how DL can be used to recognize the driver’s drowsiness situation. This is also important to vehicle safety. Part II. DL for effective vehicle communications: Vehicle networks consist of vehicle-to-vehicle and vehicle-to-roadside communications. Intelligent vehicle networks require the flexible selection of the best path across all vehicles, the adaptive sending rate control based on bandwidth availability, timely data downloading from roadside base-station, etc. In this part, we have selected a few chapters on the use of DL for different network layers in V2X networks. For example, in the physical layer, DL can be used for an energy-efficient modulation parameter selections. Part III. DL for vehicle control: For each individual vehicle, many operations require intelligent control. For example, the brake needs to be applied with different strength levels depending on the distance to the obstacle and current vehicle speed. In this part, we have collected a few chapters on the use of DL for different vehicle operation control: the emission is controlled based on the road traffic situation; the charging pile load is predicted through DL; the vehicle speed is adjusted based on the camera-captured image analysis. There are hundreds of sensors in an autonomous vehicle. All those sensors’ data needs to
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viii Preface be fused based on efficient data fusion methods. Then DL could be used to further extract interpretable information from the sensor data. Part IV. DL for information management: This part covers some intelligent information collection and understanding. For example, we can use DL for energy-saving vehicle trajectory control based on the road traffic situation and given destination information; we can also natural language processing based on DL algorithm for automatic internet of things (IoT) search during driving. Vehicle crowdsensing is also a challenging issue since some vehicles do not have incentives to participate in the sensor network. In this part, we will introduce the use of DL for valuable information extraction to better understand road environment. Part V. Other applications: This part introduces the use of DL models for other vehicle controls. For example, we could use DL to recognize the driver’s behavior to determine whether the driver has a clear mind during driving. In this part, we have also discussed the simulator design issues to study the use of DL for computer vision understanding based on the video data collected by the vehicle’s cameras. Autonomous vehicles are becoming more and more popular in easingly society. The DL and its variants will play increasingly important roles in cognitive vehicle communications and control. Other machine learning models such as deep reinforcement learning will also facilitate the intelligent vehicle behavior understanding and adjustment. We expect that this book will become a valuable reference to your understanding of this critical field.
About the Editors Dr. Fei Hu is a professor in the department of Electrical and Computer Engineering at the University of Alabama. He has published over 10 technical books with CRC Press. His research focus includes cyber security and networking. He obtained his Ph.D. degrees at Tongji University (Shanghai, China) in the field of Signal Processing (in 1999), and at Clarkson University (New York, USA) in Electrical and Computer Engineering (in 2002). He has published over 200 journal and conference papers and books. Dr. Hu's research has been supported by U.S. National Science Foundation, Cisco, Sprint, and other sources. He won the school’s President’s Faculty Research Award (