Autonomous Driving
Autor Jianxin Zhao, Liang Wang, Alexey Vinelen Limba Engleză Hardback – 26 oct 2026
Autonomous driving development demands more than clever algorithms. Autonomous Driving: Technology, Business, and Regulation treats autonomy as a systems problem. It links sensing, algorithms, hardware/software platforms, data, vehicle value chains, market structure, commercialization, batteries, safety standards, and legal frameworks. Written by authors from academia and industry, the book gives readers a holistic view of how autonomous-driving systems are built, deployed, financed, and governed.
Coverage spans multiple technical topics in autonomous driving, including: localization, perception, prediction, motion planning, end-to-end learning, V2X communication, automotive operating systems and chips, data management, and cloud-based development workflows, etc. The book also examines regional market models and their legal frameworks, value-chain shifts across suppliers and platforms, business models, deployment verticals, EV batteries, the safety frameworks that shape real-world autonomy. Each topic is treated through the book's central theme: managing uncertainty across technology, business, and regulation.
Readers will also find:
- Detailed treatment of cooperative autonomous driving algorithms and platforms, and their role in future smart mobility infrastructure and transportation coordination
- Analysis of vehicle hardware and software platforms, including E/E architecture, domain controllers, SoCs, AUTOSAR, QNX, middleware, and vehicle-cloud workflows
- Examination of investment strategies, value chain, and competitive dynamics shaping the commercial landscape of the autonomous vehicle industry
- Analysis of legal frameworks governing autonomous vehicle and its operation across multiple international jurisdictions
- Domain-expert perspectives giving readers a concise practitioner view in the industry
- Integration of academic research findings with practical engineering and deployment challenges faced by industry practitioners
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Specificații
ISBN-13: 9781394340798
ISBN-10: 1394340796
Pagini: 304
Dimensiuni: 152 x 229 x 19 mm
Greutate: 0.58 kg
Ediția:1. Auflage
Editura: Wiley
ISBN-10: 1394340796
Pagini: 304
Dimensiuni: 152 x 229 x 19 mm
Greutate: 0.58 kg
Ediția:1. Auflage
Editura: Wiley
Notă biografică
Jianxin Zhao, PhD, is a postdoctoral researcher at the Karlsruhe Institute of Technology (KIT). He completed his PhD at the University of Cambridge under Prof. Jon Crowcroft. He is the author of several technical books. He designs and optimizes Applied AI systems.
Liang Wang, PhD, is an adjunct professor at University of Helsinki and Chief AI Architect at Nokia Technologies, where he designs and implements advanced analytics systems. He holds an MBA from Aalto University and a PhD from University of Helsinki.
Alexey Vinel, PhD, is a professor at the KIT as head of the Cooperative Autonomous Systems group, and the Halmstad University. He conducts research on vehicular communications, cooperative autonomous driving, and smart mobility. He is an IEEE Senior Member.
Liang Wang, PhD, is an adjunct professor at University of Helsinki and Chief AI Architect at Nokia Technologies, where he designs and implements advanced analytics systems. He holds an MBA from Aalto University and a PhD from University of Helsinki.
Alexey Vinel, PhD, is a professor at the KIT as head of the Cooperative Autonomous Systems group, and the Halmstad University. He conducts research on vehicular communications, cooperative autonomous driving, and smart mobility. He is an IEEE Senior Member.
Cuprins
Foreword xv
About the Authors xvii
Acknowledgments xix
Acronyms xxi
1 Introduction 1
1.1 What This Book Is and Is Not 2
1.2 On Autonomy Levels 5
1.3 Embrace the Uncertainty 6
1.4 On Electric Vehicles 7
1.5 Autonomous Driving: A Short History 7
Part I Technology 11
2 Algorithms: Part 1 13
2.1 Localization: Sensors 15
2.2 Route Planning: Global 18
2.3 Prediction 19
2.4 Behavior Planning and Reinforcement Learning 28
2.5 Trajectory Planning 35
3 Algorithms: Part 2 41
3.1 Environment Perception 42
3.2 "Where am I?" 49
3.3 Object Tracking 56
3.4 Route Search 59
3.5 Prediction 61
3.6 Motion Planning 63
3.7 End-to-end Autonomous Driving 65
4 Hardware Platform 69
4.1 Brief History of Automotive Chips 70
4.2 Electrical/Electronic Architecture Evolution 71
4.3 Computation Hardware 73
4.4 Hardware for Safety 77
4.5 Snapdragon Computation Platforms 81]
4.6 Communication Hardware 82
5 Software Platform 87
5.1 Software Architecture Overview 88
5.2 System Software: Hypervisor 89
5.3 System Software: Middleware 91
5.4 System Software: OS Core 94
5.5 Functional Software 97
5.6 Application Software 99
5.7 Vehicle-cloud Synergy 101
5.8 Case Study: Evolution of Apollo 102
6 Data Management 105
6.1 Data Ecosystem 106
6.2 Data Architectures 107
6.3 Data Governance 110
6.4 Data Management 111
6.5 Case Study: Cloud-based Data Management 120
7 V2X: Cooperative Driving 127
7.1 DSRC and C-V2X 128
7.2 Application Scenarios 129
7.3 Global Efforts on V2X 131
7.4 Collaborative Perception 136
Part II Business 141
8 Global Market Dynamics 143
8.1 United States: Fragmented Leadership 144
8.2 China: Policy-driven Innovation at Scale 147
8.3 Europe: Regulation-driven Advancement 149
8.4 Hybrid Models 152
9 Value Chain and Ecosystem 155
9.1 Vehicle Physical Systems 156
9.2 Vehicle Communication Infrastructure 165
9.3 Domain Controllers 168
9.4 Sensors 171
9.5 Cloud Services 172
9.6 Changing Landscape 174
10 Business Models 177
10.1 Platform-based Model 178
10.2 Mobility-as-a-service Model 179
10.3 Fleet Ownership Model 181
10.4 Subscription Model 182
10.5 Data Monetization Model 183
10.6 Preventing Model Degradation 185
11 Commercialization Tarpit 187
11.1 Funding Mechanisms 188
11.2 Commercialization Stages 190
11.3 Survivors 194
11.4 Commercialization Cycles 198
11.5 Commercial Alliance Structures 199
12 Industry Verticals 203
12.1 Autonomous Freight Trucking 206
12.2 Autonomous Shuttles 209
12.3 Urban Distribution 212
12.4 Special Scenarios 214
12.5 Robotaxi 217
13 Battery 223
13.1 Battery as System 224
13.2 Raw Materials 225
13.3 Cell Components 229
13.4 Manufacturing 232
13.5 Cost Reduction 235
13.6 Recycling 237
13.7 Battery Safety 240
13.8 Reflection 243
14 Safety Standards and Regulations 245
14.1 Functional Safety 246
14.2 Safety of the Intended Functionality 257
14.3 Cybersecurity 260
A Autonomous Driving Legal Frameworks of the European Union and China: A Comparative Study 263
A.1 Legislative Progress Overview 263
A.2 Product Approval and Road Operation 266
A.3 Liability Attribution 269
A.4 Data Governance 274
Index 279
About the Authors xvii
Acknowledgments xix
Acronyms xxi
1 Introduction 1
1.1 What This Book Is and Is Not 2
1.2 On Autonomy Levels 5
1.3 Embrace the Uncertainty 6
1.4 On Electric Vehicles 7
1.5 Autonomous Driving: A Short History 7
Part I Technology 11
2 Algorithms: Part 1 13
2.1 Localization: Sensors 15
2.2 Route Planning: Global 18
2.3 Prediction 19
2.4 Behavior Planning and Reinforcement Learning 28
2.5 Trajectory Planning 35
3 Algorithms: Part 2 41
3.1 Environment Perception 42
3.2 "Where am I?" 49
3.3 Object Tracking 56
3.4 Route Search 59
3.5 Prediction 61
3.6 Motion Planning 63
3.7 End-to-end Autonomous Driving 65
4 Hardware Platform 69
4.1 Brief History of Automotive Chips 70
4.2 Electrical/Electronic Architecture Evolution 71
4.3 Computation Hardware 73
4.4 Hardware for Safety 77
4.5 Snapdragon Computation Platforms 81]
4.6 Communication Hardware 82
5 Software Platform 87
5.1 Software Architecture Overview 88
5.2 System Software: Hypervisor 89
5.3 System Software: Middleware 91
5.4 System Software: OS Core 94
5.5 Functional Software 97
5.6 Application Software 99
5.7 Vehicle-cloud Synergy 101
5.8 Case Study: Evolution of Apollo 102
6 Data Management 105
6.1 Data Ecosystem 106
6.2 Data Architectures 107
6.3 Data Governance 110
6.4 Data Management 111
6.5 Case Study: Cloud-based Data Management 120
7 V2X: Cooperative Driving 127
7.1 DSRC and C-V2X 128
7.2 Application Scenarios 129
7.3 Global Efforts on V2X 131
7.4 Collaborative Perception 136
Part II Business 141
8 Global Market Dynamics 143
8.1 United States: Fragmented Leadership 144
8.2 China: Policy-driven Innovation at Scale 147
8.3 Europe: Regulation-driven Advancement 149
8.4 Hybrid Models 152
9 Value Chain and Ecosystem 155
9.1 Vehicle Physical Systems 156
9.2 Vehicle Communication Infrastructure 165
9.3 Domain Controllers 168
9.4 Sensors 171
9.5 Cloud Services 172
9.6 Changing Landscape 174
10 Business Models 177
10.1 Platform-based Model 178
10.2 Mobility-as-a-service Model 179
10.3 Fleet Ownership Model 181
10.4 Subscription Model 182
10.5 Data Monetization Model 183
10.6 Preventing Model Degradation 185
11 Commercialization Tarpit 187
11.1 Funding Mechanisms 188
11.2 Commercialization Stages 190
11.3 Survivors 194
11.4 Commercialization Cycles 198
11.5 Commercial Alliance Structures 199
12 Industry Verticals 203
12.1 Autonomous Freight Trucking 206
12.2 Autonomous Shuttles 209
12.3 Urban Distribution 212
12.4 Special Scenarios 214
12.5 Robotaxi 217
13 Battery 223
13.1 Battery as System 224
13.2 Raw Materials 225
13.3 Cell Components 229
13.4 Manufacturing 232
13.5 Cost Reduction 235
13.6 Recycling 237
13.7 Battery Safety 240
13.8 Reflection 243
14 Safety Standards and Regulations 245
14.1 Functional Safety 246
14.2 Safety of the Intended Functionality 257
14.3 Cybersecurity 260
A Autonomous Driving Legal Frameworks of the European Union and China: A Comparative Study 263
A.1 Legislative Progress Overview 263
A.2 Product Approval and Road Operation 266
A.3 Liability Attribution 269
A.4 Data Governance 274
Index 279