Computing in Intelligent Transportation Systems (EAI/Springer Innovations in Communication and Computing) 303138668X, 9783031386688

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EAI/Springer Innovations in Communication and Computing

Archana Naganathan Niresh Jayarajan Mamun Bin Ibne Reaz,   Editors

Computing in Intelligent Transportation Systems

EAI/Springer Innovations in Communication and Computing Series Editor Imrich Chlamtac, European Alliance for Innovation Ghent, Belgium

The impact of information technologies is creating a new world yet not fully understood. The extent and speed of economic, life style and social changes already perceived in everyday life is hard to estimate without understanding the technological driving forces behind it. This series presents contributed volumes featuring the latest research and development in the various information engineering technologies that play a key role in this process. The range of topics, focusing primarily on communications and computing engineering include, but are not limited to, wireless networks; mobile communication; design and learning; gaming; interaction; e-health and pervasive healthcare; energy management; smart grids; internet of things; cognitive radio networks; computation; cloud computing; ubiquitous connectivity, and in mode general smart living, smart cities, Internet of Things and more. The series publishes a combination of expanded papers selected from hosted and sponsored European Alliance for Innovation (EAI) conferences that present cutting edge, global research as well as provide new perspectives on traditional related engineering fields. This content, complemented with open calls for contribution of book titles and individual chapters, together maintain Springer’s and EAI’s high standards of academic excellence. The audience for the books consists of researchers, industry professionals, advanced level students as well as practitioners in related fields of activity include information and communication specialists, security experts, economists, urban planners, doctors, and in general representatives in all those walks of life affected ad contributing to the information revolution. Indexing: This series is indexed in Scopus, Ei Compendex, and zbMATH. About EAI - EAI is a grassroots member organization initiated through cooperation between businesses, public, private and government organizations to address the global challenges of Europe’s future competitiveness and link the European Research community with its counterparts around the globe. EAI reaches out to hundreds of thousands of individual subscribers on all continents and collaborates with an institutional member base including Fortune 500 companies, government organizations, and educational institutions, provide a free research and innovation platform. Through its open free membership model EAI promotes a new research and innovation culture based on collaboration, connectivity and recognition of excellence by community.

Archana Naganathan  •  Niresh Jayarajan Mamun Bin Ibne Reaz Editors

Computing in Intelligent Transportation Systems

Editors Archana Naganathan PSG College of Technology Coimbatore, Tamil Nadu, India

Niresh Jayarajan PSG College of Technology Coimbatore, Tamil Nadu, India

Mamun Bin Ibne Reaz Universiti Kebangsaan Malaysia Bangi, Malaysia

ISSN 2522-8595     ISSN 2522-8609 (electronic) EAI/Springer Innovations in Communication and Computing ISBN 978-3-031-38668-8    ISBN 978-3-031-38669-5 (eBook) https://doi.org/10.1007/978-3-031-38669-5 © The Editor(s) (if applicable) and The Author(s), under exclusive license to Springer Nature Switzerland AG 2024 This work is subject to copyright. All rights are solely and exclusively licensed 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 imprint is published by the registered company Springer Nature Switzerland AG The registered company address is: Gewerbestrasse 11, 6330 Cham, Switzerland Paper in this product is recyclable.

Preface

The advancement of Internet of Things and artificial intelligence has led to a new dimension of transportation, namely, intelligent transportation system. Smart transportation is already a reality because of the development of cloud-based technologies as well as improvements in vehicle-to-vehicle and vehicle-to-grid infrastructure communication. These technologies seek to offer cutting-edge services pertaining to various forms of transportation and traffic control. It allows for greater user education and safer and more efficient usage of transportation systems. Computing is a broad term referred to the automation done using computers, on a higher intelligence level. Like other application, computing will bring in various advantages to the transportation field in various sub-sections including safety, driver assistance, and autonomy. Intrusion of computing into transportation has led to the integration of vehicles, road, and other users thereby improving driving experience and safer environment. The market for smart transportation is predicted to grow as a result of factors including the global demand for efficient transportation networks, government initiatives towards green energy, and increased demand as a result of rising road safety and traffic congestion issues. A few other key aspects that are projected to fuel the market’s expansion include the increased use of cloud services in transportation and the deployment of smart transportation systems in railways. The market expansion, however, is constrained by significant capital expenditures, extensive database needs for the road network, and a lack of uniform and standardized technologies. Additionally, the development of parking management systems and investments in new smart city concepts offer market participants substantial growth prospects. Smart transportation attempts to offer cutting-edge services relating to various forms of transportation and traffic control systems. It makes transportation simple and quick by using intelligent systems including wireless communications, sensing technologies, real-time data, and computing technologies. As a result, time is saved in transportation solutions like traffic management and ticketing systems. Furthermore, due to their numerous advantages, fleet operators are progressively

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adopting cutting-edge information and communication technology applications including fiber optics, GPS, and the Internet of Things. Computing is the study of reasoning, deduction, and thinking that acknowledges and makes use of the grouping, membership, and classification of distinct numbers under investigation in the real world. Because it does not need exact mathematical definitions and distinctions for the system components, it is an extension of natural heuristics and is capable of handling complicated systems. It varies from hard computing in that it is more accepting of imperfection, ambiguity, and incomplete truth than hard computing is. In actuality, the human mind serves as a model for computing. The driving premise of soft computing is to achieve tractability, robustness, and low solution cost by taking use of the tolerance for ambiguity, partial truth, and imprecision. They can provide answers to issues that are too difficult or intrinsically noisy for traditional mathematical techniques to solve. Computing applications have demonstrated two key benefits. In the beginning, it made it possible to solve nonlinear issues for which mathematical models are not available. Second, it brought human knowledge into the disciplines of computing, such as cognition, recognition, comprehension, learning, and others. As a result, it became possible to create intelligent systems like automated design systems and autonomous self-tuning systems. Although the issues facing the road sector in the actual world are technically solvable, doing so would need an enormous amount of resources, a large data base, and excessive calculation time. The issues that designers run into in the realm of transportation engineering are typically unstructured and imprecise, driven by their instincts and prior experiences. These issues are complicated in nature and have variable characteristics, making them challenging to numerically address. The term “computational intelligence” also applies to soft computing. These kinds of issues can be successfully solved utilizing soft computing approaches like genetic algorithms, fuzzy logic, artificial neural networks, etc. using programs like VISSIM, MX Road, TransCAD, etc. The computing techniques have been used in various areas of transpiration as discussed in the following section, and thereafter in the chapters to come. Accident detection and prevention Smart transportation includes accident detection and prevention, which is a critical activity for every city because an effective preventative strategy can help save lives. If drivers maintain greater concentration while on the road, accidents can be avoided. A driver can be alerted to life-or-death circumstances by an accident prevention system and take prompt action. By identifying accident-­prone locations or accidents that have already occurred in the live traffic network, accident detection can also help to reduce the number of accidents and traffic congestion. Computing techniques have shown to be particularly helpful in identifying traffic accidents, or identifying patterns that could result in new accidents and alerting drivers to prevent them. Route optimization Urban regions frequently have traffic congestion, which is only becoming worse as more vehicles are added to the road. In order to reduce traffic congestion, route

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optimization proposes the optimum path for a given destination. Reducing traffic congestion cuts down on both travel time and car emissions. Road anomalies detection Since the state of the roads has an immediate impact on many aspects of transportation, road abnormalities detection is important in smart transportation. A road anomaly detection system’s primary function is to find potholes and bumps in the road and alert drivers to them. Traffic congestion, car damage, and road accidents can all result from poor road conditions. Since some computing techniques well suited to the task of detecting road anomalies, the material presented here also leans in that direction. Infrastructure Vehicle-to-vehicle communication is an emerging area in improving the overall infrastructure of the transportation industry. The automobiles can use GPS to track their whereabouts and communicate with one another. The vehicles can share data regarding their movement, speed, and proximity to other cars while simultaneously transmitting the data to a server. Thus, sudden speed changes can be warned to oncoming traffic in advance to prevent accidents, or traffic congestion information can be shared with other vehicles to improve navigational services. Parking Applications for parking are created to efficiently monitor parking lot availability, provide users with options for reservations, and even incorporate parking detection and alerting systems. To detect a car in a parking space and transmit the information to a centralized system, many IoT devices have been employed. Additionally, several studies employ computing methods to image data to automatically find free parking spaces on a large scale. Lights The Smart Street Lights are a crucial component of a smart city and are included in the category of smart mobility services. Smart lighting can save energy while providing dynamic functionality and manageability. By including a light sensor, an IR sensor, GPS, and a wireless connection module, street lights acquire smart features. By being aware of congested locations and dynamically adjusting their light output, lamps can make densely populated areas safer while simultaneously using less energy. When a lamp breaks, the GPS can let a centralized system track its location and condition and expedite maintenance procedures. This book will hence provide an insight into the applications of computing techniques for making the transportation systems smart. It includes lane detection, safety challenges, farming vehicle, and computing in unmanned aerial vehicles and autonomous vehicles. Coimbatore, TN, India

Archana Naganathan

Contents

Assessment and Prospects for Using Digital Technologies in the Development of Transport Systems ������������������������������������������������������   1 Lesya Bozhko and Ilia Gulyi  Lane Detection in Autonomous Vehicles Using AI������������������������������������������  15 M. Saranya, N. Archana, M. Janani, and R. Keerthishree Dynamic Control, Architecture, and Communication Protocol for Swarm Unmanned Aerial Vehicles ������������������������������������������������������������  31 Tamilselvan Ganesan, Niresh Jayarajan, and B. G. Shri Varun  Visual Perception Stack for Autonomous Vehicle�������������������������������������������  51 Anthony Benedict, Niresh Jayarajan, Adarsh V. Srinivasan, and Sowmiyan Asokar  IoT-Based Unmanned Aerial Vehicle (UAV) for Smart Farming������������������  77 Tamilselvan Ganesan, Niresh Jayarajan, S. Neelakrishnan, and P. Sureshkumar  Insight Into Safety Challenges of Intelligent Transportation Systems����������  95 M. Saranya and N. Archana Index������������������������������������������������������������������������������������������������������������������  109

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Assessment and Prospects for Using Digital Technologies in the Development of Transport Systems Lesya Bozhko and Ilia Gulyi

1 Introduction 1.1 Literature Review Participants in the transport services market today are working in new conditions associated with a significant increase in competition, the complication of multimodality, the emergence of new technologies, and other requirements for speed and comfort. A powerful impetus for the development of transport systems is digitalization and the use of intelligent technologies. This is evidenced by studies carried out in different countries and for different types of transport. The introduction of intelligent and digital technologies has recently been of interest to scientists and researchers from different parts of the world, regardless of the level of penetration of digitalization into the economies of their countries. The development of business models through the use of blockchain is studied [1], and the achievements of blockchain for IoT in the context of intelligent transport are highlighted [2]. Much attention is paid to technological [3] and organizational [4, 5] problems of blockchain implementation. The influence of CRM systems on the productivity of companies is studied in [6–9]. There are publications that study the factors affecting the implementation of the IoT in transport and logistics [10] and describe the impact of IoT technologies on business [11–13]. L. Bozhko (*) Department of Management and Marketing, Emperor Alexander I St. Petersburg State Transport University, Saint Petersburg, Russian Federation e-mail: [email protected] I. Gulyi Department of Transport and Economics, Emperor Alexander I St. Petersburg State Transport University, Saint Petersburg, Russian Federation © The Author(s), under exclusive license to Springer Nature Switzerland AG 2024 A. Naganathan et al. (eds.), Computing in Intelligent Transportation Systems, EAI/Springer Innovations in Communication and Computing, https://doi.org/10.1007/978-3-031-38669-5_1

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At the same time, although some of the listed publications in one way or another touched on the use of digital technologies in logistics and supply chain management, there was no special study on the use and economic prospects of specific digital technologies in transport systems. It is necessary to assess the economic consequences of the application of these technologies for the transport sector.

1.2 Problem Statement Currently, the problem of high administrative and commercial costs in the activities of transport carriers is aggravated. It is generally accepted that the introduction of innovations is associated with significant investments. This circumstance, against the background of rising costs of transport companies, may cast doubt on the financial feasibility of using new digital technologies in transport systems. In the meantime, new and modernized digital technologies appear to be reducing the costs of transport companies. Increased competition in the transport services market is associated primarily with changes in consumer behavior, focus on speed, and optimization of the cost of transport services. The ability to quickly respond to consumer requests, interests, and values through the widespread use of digital systems of interaction, analysis, and forecasting is the most important advantage. Competitive pressure in relation to railway companies is manifested both from other modes of transport (pipeline, road) and from the subsidiaries and affiliates of the Russian Railways company. Russian transport companies at the current stage of development are entering a long-term cycle of digital transformation. One of the criteria for competitive success, in addition to operational efficiency, market share, and dynamics of freight traffic, is an understanding of clients’ needs and a company’s quick response to clients’ requests. In this regard, digital tools for interaction, analysis, forecasting changes, and making operational decisions by a company in relation to its consumers are of particular importance. Such tools, for example, are digital CRM systems [15]. The urgent task of economic science in transport is to assess the effectiveness of such systems and determine their impact on the operational parameters of transport organizations [14]. This study is devoted to the development of a methodology for assessing the level of use of digital technologies in transport systems, obtaining new data on the state of digitalization in the transport sector, identifying the prospects for using digital technologies for the development of transport companies. Given the size of investments in digital transport solutions, it is necessary to highlight the economic consequences of the use of digital technologies. Taking into account the scale of the use of railway transport in Russia, special attention should be paid to the railway industry. The aim of the study is to assess and determine the prospects for the use of digital technologies that ensure the development of transport systems.

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Based on the goal, the following tasks were set: • Assessment of the problem of administrative and commercial costs in the activities of transport carriers. • Research on the spread of digital technologies in the management of transport systems. • Assessment of the possibilities of using digital technologies in the activities of transport companies: blockchain, CRM, IoT. • Assessment of the economic consequences of using digital technologies for transport systems.

2 Methods and Materials Scientific articles and special studies on the management of transport companies, the functioning, and digitalization of the transport industry were used as a theoretical basis for the study. The subject of the research is digital technologies used in transport systems, namely, blockchain, CRM, and IoT. The object of the research is the processes of introducing digital, intelligent, and innovative technologies in transport systems. The applied research was carried out on the example of the Russian Railways. The study used data from open sources of the Russian Railways and Rosstat (Federal State Statistics Service). The study used methods of analytical comparison and statistical analysis. The research was carried out in several stages. At the first stage, the problem of administrative and commercial costs in the activities of transport carriers was studied. Based on Rosstat data, the dynamics of the share of commercial and administrative expenses in the income of Russian companies were monitored. The assessment of the share of profit before taxation in the proceeds of Russian companies by type of activity has been carried out. At the second stage, blockchain technology was analyzed for the possibility of reducing costs in the transport sector and strengthening business activity. The economic advantages of using this technology are revealed, the impact of blockchain on the global economy is assessed, and the sources of economic effect when using the blockchain are revealed. Special attention is paid to the implementation of blockchain technologies in the Russian Railways. At the third stage, the use of CRM systems for the transport industry was studied. Customer-focused digital transformation in the Russian Railways has been analyzed. An assessment of the economic effect of introducing CRM technologies has been carried out. At the fourth stage, a description of the possibilities of IoT is given and an assessment of the scale of the spread of IoT in the Russian transport sector is carried out. The list of tasks that can be solved by using IoT has been determined. The analysis of IoT functions in railway transport is carried out.

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The economic consequences for the transport industry from the introduction of digital technologies are determined. It is assumed that the use of digital technologies will reduce the administrative and commercial costs of transport companies.

3 Results At the present stage, the growth of added value in Russian transport companies, including railways, is constrained by high overhead costs. In recent years, the level of administrative, managerial, and sales costs per unit of financial result for Russian companies as a whole is quite high (in 2018–2019, commercial and administrative expenses were kept at the level of 0.12 rubles per 1 ruble of revenue). This indicator is lower in railway transport. However, there is no reduction in overhead costs in comparison with direct costs, income, and also per unit of profit received (Fig. 1). Calculations of the data for Fig. 1 were carried out according to the information of Rosstat [16]. In 2015–2019, the share of commercial and administrative expenses in revenues of railway transport companies in Russia as a whole did not change, amounting at the end of the period to 0.06 rubles per 1 ruble of revenue. Reducing direct costs does not give the desired effect of increasing the profitability of transportation as there is still a high level of overhead costs. Despite the steady growth in freight turnover in recent years, the share of pretax profit in the revenues of Russian railway companies is significantly less than in transport as a whole; as calculations based on the data [16] showed, in 2019, it was 0.07 and 0.10, respectively, for rail transport and for the transport sector as a whole. Under these conditions, transport companies are rethinking their approaches to cost management. According to research by PricewaterhouseCoopers (PwC) [17],

Fig. 1  Dynamics of the indicator “share of commercial and administrative expenses in the income of Russian companies,” for 2015–2019, rubles per 1 ruble of income

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CEOs around the world aim to restructure operations with digital assets. Over 60% of CEOs have digital transformation as their top priority. The study considers blockchain technology, IoT, and CRM from the point of view of their use in the transport sector in general and in the railway industry in particular.

3.1 Blockchain One of the ways to reduce costs and speed up business activity is blockchain technology. The technologies of distributed data registers (blockchain) are understood as digital technologies for carrying out transactions by participants in a market network in the form of a decentralized digital register, in which processes are transparent, are open to participants, and are carried out without the help of operators and intermediaries in an automatic mode. According to PwC estimates [17], in the next decade, the widespread introduction of blockchain technologies in the world will provide an increase in world GDP of 1.75 trillion US dollars per year by 2030. Figure 2 shows data on the predicted effect of blockchain from 2021 to 2030. The study highlights five sources of the expected effect when introducing blockchain technologies (Fig. 2) and considers each of them separately. Each factor is considered separately as follows: 1. Simplification of the processes of tracking goods and services (the contribution of this factor to the growth of world GDP will be 55%, according to estimates

Fig. 2  Assessment of the impact of blockchain technologies on the global economy (global GDP growth per year), billion US dollars (in 2019 prices)

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[17]). Technologies of distributed data registers significantly simplify the processes of tracking goods movement and the legal origin of goods and services. This leads to transparency and an increase in the level of trust among the participants in the supply chain. 2. Acceleration and simplification of payments, new financial instruments. Wholesale digital assets (tokens) secured by state currency as a means of payment in international settlements will significantly simplify cross-border payments in settlements for the transportation of goods and sending passengers. Transparent blockchain digital payments will reduce the risk of default or nondelivery/delayed shipment after prepayment. 3. Improving data authentication processes. Connecting customs, tax, education, insurance, and other subjects of infrastructure and maintenance to the transport blockchain will allow one to quickly receive the necessary reliable information, for example, data on timely repairs, personnel training, qualifications, and insurance, which can be available in electronic format to passengers and shippers upon online requests in the blockchain. 4. Saving time and financial costs for organizing contract work. A distinctive feature of the blockchain in comparison with Internet payment services is the ability to conclude smart contracts. In this regard, the creation of unified transport documents—a unified contract and a unified ticket—is expedient precisely in the blockchain. 5. Optimization and simplicity of customer relationships. Distributed ledgers, as a rule, are integrated with various corporate CRM platforms, offering loyalty programs, discounts, bonuses, and promotions. Another important effect is the integral “trust effect” of the participants, which makes it possible to simplify and speed up transportation processes by reducing the time spent on concluding contracts, collecting data on the reputation and reliability of counterparties. The results of statistical observations [16] confirm that a significant proportion of Russian companies are already working on the implementation of distributed ledger technologies. In 2019, about 12% of Russian transport companies worked in this direction (this is higher than the average for the economy), which is shown in Fig. 3 (compiled on the basis of data from [16]). For rail transport, this figure is lower (3.6%), which speaks only of the very beginning of work and the launch of blockchain projects in certain sections. Within the framework of the approved digital transformation strategy for the period up to 2025, the Russian Railways plans to create a number of platforms that integrate the blockchain, including multimodal freight transportation, multimodal passenger transportation, and logistics operator of e-commerce [18]. Thus, within the framework of the multimodal passenger transportation platform, it is planned to create a publicly accessible electronic environment. Payback can come within 3–4  years when the parameters of the baseline scenario for the development of the Russian Railways, stipulated by the long-term development

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Fig. 3  The share of companies among those surveyed by Rosstat that used cloud, distributed computing, virtualization, and storage systems in their activities, for 2016–2019, %

program, are reached [19]. The main components of achieving the effect that ensures the return on investment are as follows: • Growth of income due to geographic expansion and expansion of the client base. • Increasing income due to the provision of additional services. • Increase in commission income from the sale of related services (food, luggage, excursions, etc.) with the consolidation of cash flow in one place (blockchain). • Reducing labor costs when using automated sales systems and a corresponding increase in financial results. • Reducing the cost of commercial operations and part of the administrative costs in the operation of self-service. • Reducing nonproductive costs and losses due to the elimination of ineffective competition and duplication of functions of the transport market participants. • The “effect of trust,” partnership, and information exchange between platform and blockchain participants. Blockchain technology allows one to create a reliable environment for all participants in the transportation process, not only on the territory of the Russian Federation but also for foreign partners [20, p. 48].

3.2 Customer Relationship Management CRM systems are designed not only to automate a company’s operational interaction with customers but also to continuously analyze information, identify new needs, predict customer behavior, improve service, build individual business models, and select the optimal tariff setting scheme.

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In the Russian economy, there are more and more businesses with digital CRM tools every year. The dependence of the scale of CRM systems’ implementation in the industry (according to the indicator “share of companies using CRM systems”) and the rate of economic growth (according to the indicator “average annual growth in the physical volume of gross value added in 2009–2018”) was assessed. Calculations were made on the basis of data [21, 22]. It turned out that the share of companies using CRM systems increased from 3.6% in 2006 to 18.6% in 2019. For the transport system, the value of this parameter turned out to be less (12.4% in 2019), but it is possible to note a steady trend of its growth. A multifunctional system of digital customer focus at the Russian Railways is still at the implementation stage. Over the past 5 years, a unified environment for forecasting sales, generating customer-oriented solutions (taking into account characteristics of counterparties) has been formed in the transport and logistics business block of the company. The main source of economic effect in the implementation and further improvement of CRM technologies by the Russian Railways is the intensification of the physical volume of the added value of transport services. It includes: 1. Additional income growth, thanks to: • Attracting and retaining customers with a high level of income for a carrier company, offering a premium package of services and higher consumer benefits (achieved using a digital segmentation and selection system). • The sale of additional, related products and services to shippers, the formation of an integrated product (for example, insurance and customs clearance, selection of a unique type of carriage, loading and unloading operations, transport factoring, “last mile” services). • Quick response to demand, including the introduction of cross-functional sales in the CRM system (with the involvement of other market participants for transportation), the creation of a digital multimodality service. • Improving the quality of transport services, creating digital services that allow a client to choose the best transportation option, taking into account the criteria set by him/her: cost, speed, reliability, safety. 2. Reduced operating costs, thanks to: • Layoffs while automating certain operational processes. • Minimizing labor costs by automating the processes of monitoring the status and location of the cargo. • Creating virtual feedback link between a carrier and a shipper; trusted digital environment for contracting, bids, and inquiries. • Elimination of nonproductive costs and losses caused by duplication and discrepancies caused, in turn, by the functioning of unrelated digital platforms, systems, and modules.

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3.3 Internet of Things IoT is the most important end-to-end technology for the modern development of companies in all sectors of the economy. The ability to connect a large number of devices, physical assets to the Internet and to each other, makes IoT not just a consumer technology but a broad commercial application, with the help of which new products, services, technologies, and business models are created and existing ones are optimized. IoT is a global infrastructure of the digital economy that provides the ability to provide services by connecting sensors and actuators of physical and virtual “things” to each other based on information and communication technologies. The use of the obtained data in real time makes it possible to optimize processes and objects, and the use of actuators (feedback from them) makes it possible to carry out optimization without significant costs [23]. Thanks to the introduction of IoT technologies, costs are reduced and labor productivity and profitability of companies, industries, and the entire global economy increase. The statistics of Rosstat [16] make it possible to assess the scale of the spread of certain digital technologies associated with IoT in the Russian transport sector, including in the field of railway transport. In Fig. 4 (built on the basis of data [16]), one can trace the relative achievements and weaknesses in the spread of digital technologies by types of economic activities “transportation and storage,” including “railway transport.” Monitoring and control systems were used in 2019 by 16.1% of transport companies; 8.4% used geographic information systems (GIS) and

Fig. 4  The share of companies that used certain types of key digital technologies related to IoT in 2019, in % of the total number of companies surveyed by

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navigation systems; 6.4% of railway transport companies used systems for collecting, storing, processing, analyzing, modeling, and visualizing data arrays; and 6.4% used GIS and navigation systems. In comparison with general macroeconomic indicators, a significant lag is noted in the introduction of radiofrequency identification (RFID) technologies in transport.

3.4 Rosstat IoT is penetrating the transport industry very intensively and noticeably. In recent decades, the number of vehicles, especially cars, in Russia has grown several times. In such conditions, remote digital monitoring is of importance. In Russia, at the state level, the obligation of carriers to install systems for remote monitoring and traffic control for commercial transportation of passengers and delivery of dangerous goods is statutory [24]. Smart solutions for transport and logistics through IoT technologies allow solving the following tasks in a generalized way: • • • • • • • •

Transport automation and control of uncrewed vehicles. Route monitoring. Traffic notification. Monitoring of infrastructure and receiving telemetry data on the condition of vehicles and cargo. Monitoring the congestion of transport networks and highways. Tracking the location and movement of vehicles. Monitoring messages and communications at the junction of various types of transport. Creation and development of a unified information telecommunications environment for the transport sector in Russia [25].

The main functionality of IoT in railway transport is to conduct continuous predictive monitoring and ensure continuous diagnostics of the material and technical base of infrastructure and rolling stock. IoT in railways will integrate such modules as data collection via sensors; subsystems for transferring accumulated data through fixed, mobile, satellite communications; generation, storage, and processing of data; device control module, continuous data analysis, and security; and subsystem for making programmed “smart” decisions. At the Russian Railways, as part of the digital transformation strategy, IoT technology accompanies the implementation of a number of projects [18], which from an economic point of view will help to reduce operating costs, reduce the cost of maintenance and repairs, and reduce unplanned downtime and losses.

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4 Discussion Digital technologies, on the one hand, help a company to optimize or even reduce administrative and commercial costs and, on the other hand, allow them to quickly respond to new requests and customer needs. Thus, their implementation and use have an effect on both sides of a partnership. Due to the widespread adoption of blockchain technologies, the exchange and acquisition of the necessary data are accelerated, the transparency of transactions is increased, and commercial transactions are simplified and reduced in price. Does the use of digital technologies in transport contribute to economic growth? Speaking globally, assessing the macro level of the economy, the research allows one to answer the question affirmatively. This is proven by CRM systems. By comparing the indices of physical volume of gross value added and the use of CRM technologies by companies, a direct correlation has been established: the macroeconomic dynamics and the scope of application of CRM systems in most industries are closely related. In the transport industry, this relationship is also traced: • The high dynamics of economic growth of air transport (over the past 10 years +3.1% on average per year) were accompanied by a high level of implementation of CRM technologies (32%). • The average increase in the added value of land and pipeline transport organizations (+0.7% per year) corresponded to the average coverage of organizations with the studied digital technologies (11–12%). • The low and negative dynamics of water transport (−0.2%) were accompanied by a weak implementation of the relevant services. However, the implementation of CRM systems is different in different types of transport. It was found that the greatest value is observed for air (32%) and road freight (24%) transport, the smallest for water (7%) and pipeline (4%).

5 Conclusions The conducted research has shown grounds for the statement: blockchain technology will bring economic results to transport companies (including railways) and other participants in the transportation market. Blockchain in the transport sector will act in the coming decades as an important source of economic growth and stability, maintaining a competitive position, convenience, quality, and speed of transport services. The study allows one to conclude that the scale of implementing CRM technologies in the Russian transport sector is increasing every year. Thanks to modern CRM systems, carriers get to know customers better, their requests, costs, and difficulties; understand the mechanisms of maintaining and developing partnerships; focus their efforts on working with individual segments; increase customer loyalty

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and satisfaction; provide optimal service packages; and achieve maximum response to their marketing programs. IoT technologies have vast opportunities, the implementation of which gives transport companies advantages. The main functionality of IoT in railway transport is to conduct continuous predictive monitoring and provide constant diagnostics of the material and technical base of infrastructure and rolling stock. Digitalization projects of the Russian Railways company will help to reduce operating costs, reduce the cost of maintenance and repairs, and reduce unplanned downtime and losses. Each of the technologies considered allows one to solve certain management problems and get an economic effect for a transport company. Undoubtedly, digital technologies are not limited to those covered in this study. Digital technologies constantly improve, and so do opportunities for the development of transport systems. In general, the introduction and use of modern digital technologies in transport systems make it possible to ensure the growth of not only national but also world GDP.

References 1. Handbook of Research on Blockchain Technology. Edited by Saravanan Krishnan, Valentina E.  Balas, Raghvendra Kumar. Academic Press. 2020. https://doi.org/10.1016/ C2019-­0-­00935-­1. 2. M.A.  Uddin, A.  Stranieri, I.  Gondal, V.  Balasubramanian. A Survey on the Adoption of Blockchain in IoT: Challenges and Solutions. Blockchain: Research and Applications. 30 January 2021. https://doi.org/10.1016/j.bcra.2021.100006. 3. Abdurrashid Ibrahim Sankaa, Muhammad Irfan, Ian Huang, Ray C.C.  Cheung. A survey of breakthrough in blockchain technology: Adoptions, applications, challenges and future research // Computer Communications. Volume 169. 1 March 2021. Pp. 179–201 https://doi. org/10.1016/j.comcom.2020.12.028. 4. Elissar Toufaily, Tatiana Zalan, Soumaya Ben Dhaou A framework of blockchain technology adoption: An investigation of challenges and expected value // Information & Management. Volume 58, Issue 3. April 2021. 103444. https://doi.org/10.1016/j.im.2021.103444. 5. Chetan Chawla. Trust in blockchains: Algorithmic and organizational // Journal of Business Venturing Insights. Volume 14, November 2020, e00203. https://doi.org/10.1016/j. jbvi.2020.e00203. 6. Samppa Suoniemi, Harri Terho, Alex Zablah, Rami Olkkonen, Detmar W. Straub. The impact of firm-level and project-level it capabilities on CRM system quality and organizational productivity // Journal of Business Research. Volume 127, April 2021. Pp. 108–122. https://doi. org/10.1016/j.jbusres.2021.01.007. 7. Ilaria Dalla Pozza, Oliver Goetz, Jean Michel Sahut. Implementation effects in the relationship between CRM and its performance // Journal of Business Research. Volume 89, August 2018. Pp. 391–403. https://doi.org/10.1016/j.jbusres.2018.02.004. 8. Zeynab Soltani, Batool Zareie, Farnaz Sharifi Milani, Nima Jafari Navimipour. The impact of the customer relationship management on the organization performance // The Journal of High Technology Management Research. Volume 29, Issue 2, November 2018. Pp. 237–246. https://doi.org/10.1016/j.hitech.2018.10.001.

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9. Jacob Z. Haislip, Vernon J. Richardson. The effect of Customer Relationship Management systems on firm performance // International Journal of Accounting Information Systems. Volume 27, November 2017. Pp. 16–29. https://doi.org/10.1016/j.accinf.2017.09.003. 10. Andrea Rey, Eva Panetti, Roberto Maglio, Marco Ferretti. Determinants in adopting the Internet of Things in the transport and logistics industry // Journal of Business Research, 5 January 2021. https://doi.org/10.1016/j.jbusres.2020.12.049. 11. Bertrand Pauget, Ahmed Dammak. The implementation of the Internet of Things: What impact on organizations? // Technological Forecasting and Social Change. Volume 140, March 2019. Pp. 140–146. https://doi.org/10.1016/j.techfore.2018.03.012. 12. Vlad Krotov. The Internet of Things and new business opportunities // Business Horizons. Volume 60, Issue 6. November-December 2017. Pp. 831–841. https://doi.org/10.1016/j. bushor.2017.07.009. 13. Daniel Kiel, Christian Arnold, Kai-Ingo Voigt // The influence of the Industrial Internet of Things on business models of established manufacturing companies – A business level perspective // Technovation. Volume 68, December 2017. Pp. 4–19. 14. Zhuravleva N.A. Problems of introducing digital technologies in transport // Transport of the Russian Federation. 2019. No. 3 (82). Pp. 19–22. 15. Efimova O.V., Murev D.I. Substantiation of CRM effectiveness // World of transport. 2016. Vol. 14. No. 1. Pp. 90–98. 16. Unified interdepartmental information and statistical system: Official statistical indicators. [Electronic resource]. URL: https://fedstat.ru. 17. It’s time to trust. Trillions of Dollars Reasons to Look at Blockchain in a New Way: PricewaterhouseCoopers (PwC) Research, 2020. 23 p. [Electronic resource]. URL: https://pwc.ru. 18. Charkin E.I.  On the implementation of the digital transformation strategy of the Russian Railways company // Railway transport. 2020. No. 2. Pp. 66–70. 19. On the approval of the long-term development program of the Russian Railways company until 2025: the Order of the Government of the Russian Federation dated March 19, 2019, No. 466-r [Electronic resource]. URL: http://government.ru/docs/36094. 20. Development directions of the Russian legs of international transport corridors: Monograph / T.Y.  Ksenofontova, L.M.  Bozhko, V.F.  Volkov, A.I.  Goncharov. Yelm, WA, USA: Science Book Publishing House, 2020. 112 p. 21. Results of federal statistical observation in form No. 3 “Data on the use of information and communication technologies and the production of computers, software, and services in this area”. URL: https://rosstat.gov.ru/folder/14478 (accessed: 30 Dec 2020). 22. National accounts of Russia in 2014-2018: Rosstat, 2019. 23. On the approval of the development of Internet of Things on the territory of the Russian Federation: the Order of the Ministry of Digital Development, Communications and Mass Media of the Russian Federation of March 29, 2019, No. 113 [Electronic resource]. URL: https://digital.gov.ru/ru/ documents/6410. 24. Prospects for the development of Internet of things in Russia [Electronic resource]. URL: https://www.pwc.ru. 25. Eight Key Technologies. Research by PriceWaterhouseCoopers, October 2017 [Electronic resource]. URL: https://www.pwc.ru.

Lane Detection in Autonomous Vehicles Using AI M. Saranya, N. Archana, M. Janani, and R. Keerthishree

1 Introduction A vehicle that is self-driving has the ability to sense its surroundings. The term “autonomous” aptly characterizes its ability to function without the need for human intervention. A self-driving vehicle can be utilized in the same way as a conventional vehicle [1]. It meets all of the specifications and does all of the tasks that a skilled human driver can. The following is a list of the six stages of driving automation. • Level 0 – There is no automation (manual control that is human performs all the driving tasks). • Level 1 – Assistance to the driver (the vehicle features a single automated system, for example, cruise control) • Level 2 – Automated in part (the vehicle can perform steering and acceleration but the human can monitor the task and can take the control at any time) • Level 3 – Conditional automation (vehicle can perform driving tasks, but human override is still required) • Level 4 – High levels of automation (the vehicle performs all driving tasks under specific circumstances and geo-fencing is required) • Level 5 – Complete automation (vehicle performs all driving tasks under all conditions and zero human attention or intervention is required) Object identification aids the software in adhering to traffic laws and navigating the road while avoiding potential hazards. Autonomous cars are run by using M. Saranya (*) · M. Janani · R. Keerthishree Department of I&CE, PSG College of Technology, Coimbatore, TN, India e-mail: [email protected] N. Archana Department of EEE, PSG College of Technology, Coimbatore, TN, India © The Author(s), under exclusive license to Springer Nature Switzerland AG 2024 A. Naganathan et al. (eds.), Computing in Intelligent Transportation Systems, EAI/Springer Innovations in Communication and Computing, https://doi.org/10.1007/978-3-031-38669-5_2

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software, and in addition, it has sensors, actuators, complex algorithms, machine learning systems, and powerful processors to execute software. Using a range of sensors dispersed throughout the vehicle, autonomous vehicles develop a map of their environment on their own. Radar sensors keep track of what’s going on around you. Traffic lights are searched, road signs are read, surrounding vehicles are tracked, and persons and objects are searched for using video cameras. Lidar (light detection and ranging) sensors estimate distances, detect road borders, and determine which lane the car is in by reflecting light pulses from the surroundings around the vehicle. Curbs and other vehicles are detected using ultrasonic sensors in the wheels while parking. The car’s actuators, which regulate braking, acceleration, and steering, are managed by sophisticated software that analyzes all of the sensory data, draws a clear path, and delivers commands to the actuators. The application uses hard-coded rules, avoidance algorithms, predictive modelling, and object identification to comply with traffic laws and navigate the route while avoiding barriers.

1.1 Role of AI in Autonomous Vehicles Although autonomous vehicles are meant to drive like human drivers, they are required to do so. All of the foregoing is only conceivable if these vehicles are given sensory, cognitive, and executive functions similar to those used by humans when driving automobiles. According to a recent study, around 2020, there will be connection over 250 million cars, and autonomous vehicles are being equipped with cameras, sensors, and communication systems to compete with the vehicle to generate enormous amounts of data, which, when combined with AI, gives the vehicle the ability to see, hear, think, and make decisions in the same way that human drivers do. The entering input produces a loop that is usually referred to as the Perception Action Cycle [2]. The data is fed into the AI, which then makes decisions and allows the autonomous vehicle to carry out particular tasks. The AI becomes more clever as the number of Perception Action Cycles increases, resulting in increased accuracy in making driving decisions, especially in complicated driving circumstances. This procedure can be divided into three parts: • In-vehicle data gathering and communication system • Autonomous driving platform • AI-based functionalities in autonomous vehicles

1.2 In-Vehicle Data Collection and Communication System Self-driving automobiles collect data from sensors, radars, and cameras. The Digital Consortium assembles all of the data, allowing autonomous vehicles to see, hear, and feel the road, other vehicles, and all other objects and barriers on the road in the

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same manner that human drivers do. The data is then processed on supercomputers, with data connection methods used to securely transmit sensitive data, such as input to the autonomous driving cloud platform.

2 AI-Based Functions in Autonomous Vehicles The autonomous vehicle begins to detect objects on and near the road based on the decisions, navigates through traffic without the assistance of a human, and safely arrives at its destination. AI-based functional systems included in autonomous vehicles include voice and speech recognition, gesture controls, eye tracking, and other driving monitoring systems. The aforementioned functions are likewise carried out on the autonomous driving platform based on AI judgments.

3 Lane Detection in Autonomous Vehicles In a self-driving car, lane detection is critical. In the realm of autonomous driving, high-precision lane/vehicle localization is crucial. Lane detection in intelligent cruise control environments for lane departure warning, modelling the route, and so on. Lane detection in intelligent cruise control environments for lane departure warning, modelling the way, and so on. Lane detection is an important topic in machine learning and computer vision, and it has been used in intelligent vehicle systems. The lane detection system is based on lane markers in a complicated environment, and it is utilized to accurately determine the vehicle’s position and trajectory in relation to the lane. At the same time, the lane departure warning system relies heavily on lane detection. Edge detection and line detection are the two key steps in the lane detecting process. In autonomous cars, lane detection can be done using a computer vision algorithm and the Python programming language. In this research, feature-based boundary detection was used to approach lane detection, as illustrated in Fig. 1.

3.1 Computer Vision One of the numerous AI disciplines is computer vision. Computer vision technology can automate visual interpretation from a constant stream of pdfs, photos, videos, and text exchanges using artificial intelligence (AI) and machine learning (ML) approaches. In other words, computer vision imitates certain aspects of human vision, but it is far faster and more precise in some cases. Computer vision is a field that spans multiple disciplines. In order to process images, it employs relevant concepts from neurobiology, signal processing, and information engineering. To

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Road Detection Approaches

Feature based on boundary detection

Region based features

Combined boundary and region based

Edge detection based approach

Texture based Features

Vanishing point based boundary detection

Color based features

Hough transform based approach

Intensity Invariant Features

Fig. 1  Road detection approaches

achieve automated visual understanding, computer vision requires the creation of either a theoretical or algorithmic foundation.

3.2 Python and Computer Vision Python is an interpreted, high-level, general-purpose programming language that allows novice and expert programmers to quickly translate their ideas into code. Python is the most widely used, mature, and well-supported programming language in the field of machine learning, with the majority of developers using it for CV. The technique known as computer vision allows computers to recognize things in digital photos or movies. Developers can automate activities involving visualization by using Python to do CV. Other programming languages typically support computer vision; however, Python has a significant lead. Python is used for computer vision because it offers the following features: • • • • •

Easy coding Easy prototyping Numerous machine learning libraries It is open source. It is directly integrated with Web frameworks.

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4 Proposed Methodology Lane detection in autonomous vehicles can be accomplished via AI, machine learning, neural networks, and other methods. Lane detection is proposed in this paper utilizing the OpenCV approach, which is an open-source computer vision technique that is part of artificial intelligence. Because of its many built-in image processing tools, the OpenCV approach is used [3]. It is portable and uses the Python programming language, as well as having a very short run time, because it is an open-source platform. In this section, we’ll go through the specifics of the lane detecting process. To achieve the lane marker, there are eight stages to follow. Figure 2 illustrates them (Fig. 3).

4.1 Acquiring Image from Video Webcams can be immediately connected to live video capturing using OpenCV. The webcam’s input will be in the form of video, which will be broken into frames in order to produce images for subsequent processing. The decoded video frames will be used to create the visual sequence. Figure 4 illustrates an example of a video image that has been ready for preprocessing [5].

4.2 Image Preprocessing To get the final lane marking, three processes are used to preprocess the image collected from the video frames. The image is first converted to gray scale and then filtered for noise reduction, and finally, canny edge detection is used as the final stage in the preprocessing process. 1. Grayscale conversion: The input image is presented in Fig. 4. The RGB input image from the video frame must be transformed to gray scale before being processed. It is easier to process a picture in a single channel (gray scale) than it is to process it in three channels (RGB format). After converting the color image to black and white, noise can be decreased. Grayscale image of Fig. 4 is presented in Fig. 5.

Fig. 2  Methodology for lane detection

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Inputvideo

Acquire an Image

Image Pre Processing • Gray scaleconversion • Noise reduction • Canny edgedetection

Region of Interest

Top view image transform

Hough transform

Least Square approximation Fig. 3  Generic block diagram

2. Noise reduction: Gaussian filter is used to reduce noise in the grayscale input image. Noise reduction is used to smooth out the image and erase any artificial edges. The Gaussian filter blurs the image or removes unwanted noise. The noise in the grayscale image is reduced using a Gaussian filter in lane detection. The image after employing a Gaussian filter to reduce noise is shown in Fig. 6.

Lane Detection in Autonomous Vehicles Using AI

Fig. 4  Road lane image

Fig. 5  Grayscale image

Fig. 6  Image after noise reduction

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Fig. 7  Image after canny edge detection

Fig. 8  Mask image

3. Canny edge detection: Canny edge detection is a method for detecting the blurred image’s gradients in all directions. It also recognizes edges with a high degree of sensitivity. Figure 7 displays the image after canny edge detection and conversion to a colored canny image.

4.3 Region of Interest This stage is carried out to determine the image’s dimension, which includes the image’s lane region. Using the AND operator, the selected region is masked. Between each canny image pixel and the mask image, an AND operation is done. The mask is made with a 255-intensity white zone of interest. In Fig. 8, you can see the mask image. As seen in Fig. 9, it masks the canny image and solely focuses on the region of interest.

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Fig. 9  Canny image after masking

Position of real camera

Position of virtual camera

Camera image

Field of view

Fig. 10  Illustration of top-view transform [8]

4.4 Top-View Transform In lane detection and fitting, the top-view transform is quite useful. It aids in the computation of lane parameters and offset errors. The mapping is done with the camera pitch angle, height, and road features in mind. The top-view transform is based on the inverse perspective transform and works by mapping the pixels of the front-view image to the top-view image. The top-view image transform is illustrated in Fig. 10 and the top-view image of the input picture is illustrated in Fig. 11.

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Fig. 11  Image after top-view transform

4.5 Hough Transform The lane lines are detected using the Hough transform, which is used to detect straight lines. The probabilistic Hough line transform is used. The straight lines are given by Eq. (1), and the line can be displayed as a single dot in the Hough space using the values of y-intercept and slope. There are numerous lines that can pass through a point, but only one consistent line will pass through both points. The Hough space is divided into grids, with bins corresponding to intercept and slope in each grid. The lane line marking is represented by the grid with the most bins. The output image following the Hough transform is shown in Fig. 12.

y  mx  b

(1)

5 Software Result In driving assistance systems, lane and object detection may be a key component of collision avoidance. With day-by-day increase in traffic, there is a demand in high security and comfort is required for driving, so new technologies are required. Computer vision is one of the techniques that can be used to support the driver in complex situations in order to increase his security and comfort. Lane tracking functionality lies on the first layer of autonomous cars. Many sensors are often used for obstacle detection and lane detection, like laser, radar, and vision sensors. To detect road boundaries and lanes, computer vision is the principal approach using

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Fig. 12  Output after Hough transform

Fig. 13  Image frame from input video

vision system on the vehicle. The system acquires the front view employing a camera mounted on the vehicle and then applying few processes so as to detect the lanes and objects. A versatile methodology is employed so as to detect the lanes and objects. The software findings for each stage addressed in relation to the suggested methodology are presented in this section. Obtaining image frames from the supplied video is the initial stage in lane detection. The image frame derived from the input video is shown in Fig. 13. The grayscale image of the input image is obtained next. The grayscale version of the input image in Fig. 13 is shown in Fig. 14. The image’s noise needs to be reduced next. To minimize noise, a Gaussian filter is used.

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Fig. 14  Image after grayscale conversion

Fig. 15  Noise-reduced image

Figure 14 is a grayscale converted image that has been filtered to remove noise, and the result is presented in Fig. 15. Using canny edge detection, the next step is to detect the image’s gradients. The canny image is shown in Fig. 16. The canny edge detected image must now be masked. The only lines on the road that are masked are yellow and white. Figure 17 illustrates the masked canny image. In addition, the

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Fig. 16  Image after canny edge detection

Fig. 17  Masked canny image

image’s region of interest is determined, as shown in Fig. 18. To get flawless lane lines, the region of interest is veiled once more. The image of the masked region of interest is depicted in Fig.  19. The final step is to apply Hough transform to the masked ROI image to get the final lane lines. Hough transform is applied to Fig. 19, and the final output lane lines are obtained and it is shown in Fig. 20a–d.

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Fig. 18  Region of interest

Fig. 19  Masked ROI

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Fig. 20  Final output

6 Conclusion Advanced driver assistance systems (ADAS) are growing in popularity as the number of self-driving automobiles grows. Road lane detection is one part of automation for these self-driving cars. In the event of a dangerous lane shift, an on-board system with this functionality can warn the driver. Lane detection is also useful for determining the layout of a road and the geometry of the surrounding scene in order to design a route. In the feature-based approach, low-level edge features are typically used for lane boundary detection. In this project, lane detection is done using OpenCV technique. The input is obtained from webcam in the form of video and it is divided into frames to obtain images for further processing. The image obtained from the video frames are preprocessed using three steps: gray scale, Gaussian filter, and canny edge detection. The dimension of the image that contains the lane

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region in the image is obtained by masking the image using AND operator. Then lane detection and fitting are done using top-view transform. And finally, the straight lines are detected using Hough transform and the curved lines are detected using least square algorithm. For future advancements, the accuracy of the output obtained can be increased by using a real-time simulator like CARLA.  Along with lane detection, object detection also can be added to prevent accidents in autonomous vehicles. Also, a cost-effective mobile app can be developed for this simulator by which we can read the information easily by using our smartphones itself.

References 1. Zhong-xun Wang, Wenqi Wang, “The research on edge detection algorithm of lane”, EURASIP Journal on Image and Video Processing, October, 2018. 2. Jamel Baili, MehrezMarzougui, et  al., “Lane Departure Detection using Image Processing Techniques”, Conference Paper, March, 2017. 3. VanQuang Nguyen, Heungsuk Kim, et  al., “A study on real time detection method of lane and vehicle for lane change assistant system using vision system on highway”, Engineering Science and Technology, an International Journal, July, 2018. 4. Othman OmranKhalifa, Sheroz Khan,” Real time lane detection for autonomous”, Conference Paper, June, 2008. 5. Aditya Singh Rathore, “Lane detection for Autonomous vehicles using OpenCVLibrary”, International Research Journal of Engineering and Technology, January 2019. 6. Md. RezwanulHaque, Md. Milon Islam, Kazi Saeed Alam, et al., “A Computer Vision based lane detection approach”, an International Journal of Image, graphics and signal processing, March,2019. 7. Vipul H.  Mistry, Dr. Ramji Makwana, “Survey: Vision based Road Detection Techniques”, Internal Journal of Computer Science and Information Technologies, 2014. 8. ByambaaDorj and DeokJin Lee, “A precise lane detection algorithm based on top view transform and least square approaches”, Research article, November, 2015.

Dynamic Control, Architecture, and Communication Protocol for Swarm Unmanned Aerial Vehicles Tamilselvan Ganesan, Niresh Jayarajan, and B. G. Shri Varun

1 Introduction Drones have progressed from toys to sophisticated flying machines, with several application fields following, including monitoring, surveillance, species identification, rescue application and deliver products. Actually, Amazon’s R&D lab announced that drones are being tested (and awaiting DGCA approval to popularize) for package delivery in India through the Amazon Prime Air scheme, in order to provide faster delivery with lower costs and lower emissions. A swarm Unmanned Aerial Vehicles (UAV) is a group of aerial robots that collaborate to achieve a common goal [1, 2]. Each UAV in a swarm is driven by a specified number of propellers and is capable of hovering vertically, taking off, and landing. The UAVs motion is controlled manually, via remote control operations, or autonomously, using processors installed in the UAV. UAVs are commonly used for military purposes, but their civilian uses have recently gained notice. Indeed, low-­ cost UAV and their swarm offer a viable platform for creative research initiatives and eventual commercial applications that will benefit people at work and in their daily lives. The general outlook of swarm UAV is shown in Fig. 1. UAV swarms can be classed in a variety of ways: Fully autonomous and semiautonomous swarms. From another perspective, the classification can be divided into single-layered swarms with each UAV acting as a leader and multilayered swarms with a single dedicated UAV leader for group of UAVs at each layer reporting to their leader UAV at a higher layer; the highest layer in this hierarchy is a ground-­ based server [3–5]. Each UAV in a swarm can have specific data gathering and

T. Ganesan (*) · N. Jayarajan · B. G. Shri Varun Department of Automobile Engineering, PSG College of Technology, Coimbatore, Tamil Nadu, India © The Author(s), under exclusive license to Springer Nature Switzerland AG 2024 A. Naganathan et al. (eds.), Computing in Intelligent Transportation Systems, EAI/Springer Innovations in Communication and Computing, https://doi.org/10.1007/978-3-031-38669-5_3

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Fig. 1  General swarm outlook

Fig. 2  Applications of UAV

processing duties with enough computational power to complete these tasks faster. Its primary processing occurs on a more powerful base station or in cloud. The purpose of this work is to (1) investigate the properties of drones and drone swarms and (2) explain current technologies on linear- and nonlinear-based controllers. This paper is organized as follows: Section 2 discusses the various drone application in current scenarios. Section 3 discusses UAV categorization. Section 4 describes the UAV dynamics. Section 5 investigates the communication architecture of swarm UAV. Section 6 discusses the currently available various routing protocols in swarm UAV communication. Finally, Section 7 draws conclusions.

2 Applications The technology advancement in the sensors integrated in UAVs for a variety of new purposes, enabling the development of a new kind of swarm unmanned operations applications and services, is shown in Fig. 2. This section briefly discusses the most common applications of UAV.

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2.1 Surveillance and Monitoring Surveillance is the monitoring of an individual, a group of individuals, behaviors, activities, infrastructure, or structures in order to gather, influence, manage, or steer information. Surveillance techniques include camera observation, GPS monitoring, signal detection, and biometric surveillance. These techniques are applied in  various surveillance tasks such as  national border monitoring, constructions, electric grid inspection, traffic and environmental monitoring, and other common surveillance jobs. A typical monitoring environment may be big and dispersed [6, 7]. Traditional manual surveillance methods have a tough time quickly locating and reaching the sites of concern or defects in facilities. Furthermore, conventional surveillance is a labor-intensive task: Numerous repeated actions in diverse job circumstances necessitate a large number of people, and labor expenses rise year after year.

2.2 Leisure Pursuit There is a growing interest in operating lightweight UAVs (weighing