Autonomous vehicles and systems : a technological and societal perspective

This book captures multidisciplinary research encompassing various facets of autonomous vehicle systems (AVS) research and developments. The AVS field is rapidly moving towards realization with numerous advances continually reported. The contributions to this field come from widely varying branches...

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Bibliographic Details
Other Authors: Sethi, Ishwar K., 1948- (Editor)
Format: eBook
Language: English
Published: [United States] : River Publishers, 2023.
Series: River Publishers series in automation, control and robotics.
Subjects:
Automated vehicles.
Automated vehicles > Social aspects.
Intelligent transportation systems.
Intelligent transportation systems > Social aspects.
elektronické knihy
electronic books
ISBN: 9788770228503
8770228507
9781003810674
1003810675
9781032629537
1032629533
9781003810704
1003810705
877022885X
9788770228855
1523156406
9781523156405
Physical Description: 1 online resource (200 pages).

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Table of contents

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245 0 0 |a Autonomous vehicles and systems :  |b a technological and societal perspective /  |c editor, Ishwar K. Sethi. 
264 1 |a [United States] :  |b River Publishers,  |c 2023. 
300 |a 1 online resource (200 pages). 
336 |a text  |b txt  |2 rdacontent 
337 |a computer  |b c  |2 rdamedia 
338 |a online resource  |b cr  |2 rdacarrier 
490 1 |a River Publishers series in automation, control and robotics 
506 |a Plný text je dostupný pouze z IP adres počítačů Univerzity Tomáše Bati ve Zlíně nebo vzdáleným přístupem pro zaměstnance a studenty 
520 |a This book captures multidisciplinary research encompassing various facets of autonomous vehicle systems (AVS) research and developments. The AVS field is rapidly moving towards realization with numerous advances continually reported. The contributions to this field come from widely varying branches of knowledge, making it a truly multidisciplinary area of research and development. The topics covered in the book include: • AI and deep learning for AVS • Autonomous steering through deep neural networks • Adversarial attacks and defenses on autonomous vehicles • Gesture recognition for vehicle control • Multi-sensor fusion in autonomous vehicles • Teleoperation technologies for AVS • Simulation and game theoretic decision making for AVS • Path following control system design for AVS • Hybrid cloud and edge solutions for AVS • Ethics of AVS. 
505 0 |a Preface xv List of Figures xvii List of Tables xxxi List of Contributors xxxiii List of Abbreviations xxxvii 1 Introduction 1 1.1 Introduction 1 1.2 Brief History of Autonomous Vehicles 3 1.3 About the Book 4 2 Deep Learning for Autonomous Vehicles and Systems 9 2.1 Review of Deep Learning Models for AVS 10 2.1.1 Convolutional neural networks 10 2.1.2 Recurrent neural networks 12 2.1.3 Graph neural networks 14 2.1.4 Deep Q-networks 16 2.2 Tasks in Autonomous Vehicles and Systems 17 2.2.1 Perception 18 2.2.2 Mapping and localization 23 2.2.3 Path planing 25 2.2.4 Motion control learning 30 2.3 Future Trends and Challenges 32 2.3.1 Reliability 32 2.3.2 Computational and energy efficiency 33 2.3.3 Data sparsity 34 2.3.4 Human factors in AVS 35 2.4 Conclusions 36 3 Toward Autonomous Vehicles and Systems - Potentials and Challenges of Artificial Intelligence 49 3.1 Introduction 50 3.2 Modeling and Simulation 51 3.2.1 Vehicle models 51 3.2.1.1 Linear single-track model 51 3.2.1.2 Nonlinear single-track model 52 3.2.1.3 Linear roll and pitch model 53 3.2.2 Simulation setup 54 3.2.3 Data generation 59 3.2.4 Model training for the automated lane change 60 3.2.5 Model validation for the automated lane change 62 3.3 Results 65 3.3.1 Differences in vehicle model quality 65 3.3.2 Definition of the model boundaries 68 3.3.3 Development of a driver assistance system based on model quality 71 3.3.3.1 Data generation and model training 71 3.3.3.2 Evaluation of the trained support vector machines 73 3.3.3.3 Evaluation of the results 74 3.4 Conclusion 76 4 Autonomous Vehicle Steering through Convolutional and Recurrent Deep Learning 83 4.1 Introduction 84 4.2 Data Collection 86 4.2.1 Integrating software modules with the vehicle 88 4.3 Model Training 90 4.3.1 Deep learning fundamentals 90 4.3.2 Model structure 91 4.3.3 Training techniques 93 4.4 Results 96 4.4.1 Original CD (CNN+DNN) 96 4.4.2 Simple RNN 97 4.4.3 CD with new data and interchangeable pretrained networks 98 4.4.4 CDR with new data 101 4.5 Discussion and Future Work 104 5 2D and 3D Pose Estimation for Gesture Recognition in Deeplearning-driven Human-vehicle Leader-follower Systems 113 5.1 Introduction 114 5.1.1 Leader-follower background 114 5.1.2 Gesture recognition background 114 5.1.3 Previous work 115 5.2 Bipartite Mapping 117 5.3 Gesture Recognition 118 5.3.1 How to teach a machine 118 5.3.2 Gesture classification 119 5.3.3 Trials with a CNN 119 5.3.4 Modular data stream pipeline design 120 5.3.5 2D pose estimation 122 5.3.6 3D pose estimation 124 5.4 Integration of Models with the ACTor 1 Vehicle 125 5.4.1 ACTor 1 overview 125 5.4.2 ROS fundamentals 126 5.4.3 ROS node design and implementation 127 5.5 Testing Results 129 5.5.1 Results using the pipeline with 2D posenet 129 5.5.2 Bipartite mapping results 133 5.5.3 3D pose estimation results 133 5.6 Discussion 135 5.6.1 Summary 135 5.6.2 Future work 137 6 Adversarial Attacks and Defenses on Autonomous Vehicle Systems 143 6.1 Introduction 144 6.2 Attacks 147 6.2.1 White-box attacks 147 6.2.2 Black-box attacks 150 6.2.3 Patch attacks 152 6.2.4 Natural adversarial attacks 155 6.3 Defenses 156 6.3.1 Preprocessing defenses 156 6.3.2 Input-space sampling 159 6.3.3 Adversarial training 161 6.3.4 Certified defense 164 6.4 Conclusion 168 7 Multi-sensor Fusion for Multi-target Detection and Tracking 175 7.1 Introduction 175 7.2 Sensor System in Perception Module 178 7.2.1 Camera 178 7.2.2 LiDAR 179 7.2.3 MMW radar 180 7.2.4 4D MMW radar 181 7.2.5 Conclusion 182 7.3 Datasets 183 7.4 Multi-sensor Fusion Challenges and Solutions 186 7.4.1 Challenges 186 7.4.2 Solutions 187 7.4.2.1 Solutions for Challenges 1 and 2 188 7.4.2.1.1 Data Augmentation 188 7.4.2.1.2 Feature-level fusion 192 7.4.2.1.3 Object-level fusion 195 7.4.2.2 Sensor synchronization (solutions for challenge 4) 196 7.5 Multi-sensor Fusion in Detection and Tracking 197 7.5.1 Data processing representation 197 7.5.1.1 LiDAR point cloud representation 197 7.5.1.2 Radar data representation 198 7.5.2 3D object detection 199 7.5.2.1 Image-based 3D object detection 200 7.5.2.2 Point cloud-based 3D object detection 200 7.5.2.3 Multi-sensor-based 3D object detection 200 7.5.3 Multiple object tracking (MOT) 204 7.5.3.1 Vehicle side 204 7.5.3.2 Roadside 206 7.6 Conclusion 207 8 Teleoperation for Complementing and Enhancing Autonomous Vehicles 219 8.1 Background 220 8.1.1 Human intervention 220 8.1.2 Level 5 fully autonomous driving is still far away 220 8.2 Definition of Teleoperation 222 8.2.1 Teleoperation in connected mobility 222 8.2.2 Diverse forms of teleoperation 223 8.3 Importance of Teleoperation 224 8.3.1 Challenges of Fully Autonomous Driving 225 8.3.1.1 Failures in environment perception and recognition 225 8.3.1.2 Prolonged downtime due to the unfamiliar environmental interpretation 225 8.3.2 Unforeseen sensor malfunction and invalid sensor data 226 8.4 Teleoperation Legislation and Standards 227 8.4.1 Teleoperation legislation 227 8.4.1.1 Regulations in US states 227 8.4.1.2 Other countries' regulations 227 8.4.1.3 Illustration: Regulations on teleoperation in Germany 227 8.4.2 Teleoperation Standards 228 8.5 Technical Considerations and Improvement Techniques 229 8.5.1 Technical considerations 229 8.5.2 Improvement approaches 230 8.5.2.1 Perception enhancement for operators 230 8.5.2.2 Immersive virtual driving interface 231 8.5.2.3 Enhancement directions for control systems 231 8.5.2.4 Compensation for latency 232 8.6 Assessment Standards for Teleoperation Performance 232 8.7 Distinguished Participants in Automotive Teleoperation 233 8.7.1 Automotive company and startup 233 8.7.2 Companies offering teleoperation services 239 8.8 Teleoperation Case Studies 240 8.9 Open Problems and Potential Future Directions 242 8.9.1 Open problems in teleoperation 242 8.9.1.1 Bandwidth limitations 242 8.9.1.2 Fluctuating and excessive latency 243 8.9.1.3 Other open problems in teleoperation 244 8.9.2 Potential future directions 244 8.10 Conclusion 245 9 Hybrid Edge/Cloud Solutions for Supporting Autonomous Vehicles 255 9.1 Introduction 256 9.2 Related Work 257 9.3 System Architecture 258 9.4 Performance Evaluation 260 9.5 Conclusions 264 10 Game-theoretic Decision-making for Autonomous Driving Vehicles 269 10.1 Introduction 270 10.2 Problem Definition 273 10.2.1 Modeling scenarios for unsignalized intersection 273 10.2.1.1 Observation space 274 10.2.1.2 Action space 274 10.2.1.3 Reward function 275 10.2.2 Modeling scenarios for urban lane-change 275 10.2.2.1 Observation space 277 10.2.2.2 Action space 278 10.2.2.3 Reward function 278 10.2.3 End-to-end control scheme 279 10.3 Driver Interaction Model 279 10.3.1 level k reasoning 279 10.3.2 Dueling double deep Q-network with prioritized experience replay algorithm (D3QN PER) 280 10.3.3 Combining Level-K Reasoning with D3QN PER 282 10.3.4 Setting of Algorithm Parameters 284 10.4 Low Level Control 286 10.4.1 Trajectory generation module 286 10.4.2 Lateral control 286 10.4.3 Longitudinal control 287 10.5 Simulation and Experimental Results 287 10.5.1 Unsignalized intersection case 287 10.5.1.1 Comparison of different algorithms 287 10.5.1.2 Simulation results 288 10.5.1.3 Hardware Implementation 290 10.5.2 Urban lane-change case 292 10.5.2.1 Compare the trained policy with human drivers 292 10.6 Conclusions 294 10.6.1 Appendix 295 11 State of Modeling and Simulation in Autonomous Vehicles 303 11.1 Introduction 303 11.2 Building a world model in simulation 306 11.2.1 Components of a scenario 307 11.2.1.1 Defining scene 307 11.2.1.2 Defining situation 311 11.2.1.3 Defining scenario 314 11.2.2 Scenario descriptions in frameworks 315 11.2.2.1 PEGASUS and related projects - Germany 315 11.2.2.2 SAKURA - Japan 319 11.2.2.3 Other relevant frameworks 322 11.3 Sensor simulation - Inputting data into AV pipeline 322 11.3.1 Camera 323 11.3.2 LiDAR 326 11.3.3 Radar 328 11.4 Challenges and opportunities in AV simulation 329 12 Path Following Control System Design and Analysis of Autonomous Vehicles 335 12.1 Introduction 336 12.2 Path Following Autonomous Vehicle Model 338 12.3 Control System Design 339 12.3.1 PID control 339 12.3.2 Linear quadratic optimal control 340 12.3.3 LQR and LQI controllers for autonomous path following 344 12.4 Simulation Model 346 12.5 Simulation Studies 352 12.6 Conclusion 357 13 Public Transport Travel Time Prediction using Sequential Forward Floating Selection Algorithm and Stacked Autoencoder 361 13.1 Introduction 362 13.2  
505 0 |a Related Works 363 13.3 Proposed Approach 365 13.4 Experimental Framework and Validation 370 13.4.1 Dataset description 370 13.4.2 Validation 371 13.4.2.1 Results for research question 1 373 13.4.2.1.1 Experimental results with full dataset 373 13.4.2.1.2 Experimental results with filtered dataset 373 13.4.2.2 Results for research question 2 375 13.5 Conclusion and Future Work 380 14 A Look Into the Ethics of Autonomous Vehicles Systems (AVS) 387 14.1 Introduction 387 14.2 AVS Internal Ethics 388 14.2.1 How do AVS make decisions? 388 14.2.1.1 Quantifying consequences of decisions 390 14.2.1.2 What decisions should they make? 392 14.2.2 Ethical frameworks...forAVS? 393 14.2.2.1 Deontology 393 14.2.2.2 Consequentialism 394 14.2.2.3 Utilitarianism 395 14.2.2.4 Hedonism 395 14.2.2.5 Hedonistic utilitarian 396 14.2.2.6 Where do corporations stand? 396 14.2.3 Growing ideas of other ethical frameworks 397 14.2.3.1 Principle of double-effect 398 14.2.3.2 Rawlsianism 398 14.2.4 Do AVS need ethics? 398 14.2.4.1 No ethics in AVS 398 14.2.4.2 Human decision and control 399 14.2.5 What would we choose? The moral machine experiment 400 14.3 AVS External ethics 401 14.3.1 Overview of AVS impact 401 14.3.2 Socioeconomic impact of AVS 401 14.3.3 Who is at fault when an AVS crashes? 403 14.3.4 Should AVS develop ... 
590 |a Knovel  |b Knovel (All titles) 
650 0 |a Automated vehicles. 
650 0 |a Automated vehicles  |x Social aspects. 
650 0 |a Intelligent transportation systems. 
650 0 |a Intelligent transportation systems  |x Social aspects. 
655 7 |a elektronické knihy  |7 fd186907  |2 czenas 
655 9 |a electronic books  |2 eczenas 
700 1 |a Sethi, Ishwar K.,  |d 1948-  |e editor. 
776 0 8 |i Print version:  |t AUTONOMOUS VEHICLES AND SYSTEMS.  |d [Place of publication not identified] : RIVER PUBLISHERS, 2023  |z 877022885X  |w (OCoLC)1389879071 
830 0 |a River Publishers series in automation, control and robotics. 
856 4 0 |u https://proxy.k.utb.cz/login?url=https://app.knovel.com/hotlink/toc/id:kpAVSATSPA/autonomous-vehicles-and?kpromoter=marc  |y Full text 

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