Autonomous Driving: Technical, Business and Regula tory Landscape

Technology, Business, and Regulation
1. Auflage 2026
ISBN: 978-1-394-34079-8
Verlag: John Wiley & Sons Inc

Buch, Englisch

Navigate autonomous vehicle technology, business, and regulatory frameworks together

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

Industry professionals, graduate students, researchers, investors, and policymakers seeking a clear map of autonomous driving will find this book connects technical depth with the business and regulatory constraints that shape deployment. It equips readers to understand the field as a working ecosystem, not as a single breakthrough technology.

Autoren/Hrsg.

Zhao, Jianxin

Wang, Liang

Vinel, Alexey

Fachgebiete

• Technische Wissenschaften Verkehrstechnik | Transportgewerbe Fahrzeugtechnik
• Sozialwissenschaften Soziologie | Soziale Arbeit Spezielle Soziologie Wirtschaftssoziologie, Arbeitssoziologie, Organisationssoziologie
• Mathematik | Informatik EDV | Informatik Informatik Künstliche Intelligenz

Inhaltsverzeichnis

Foreword
Acknowledgments
Acronyms

1 Introduction 1
1.1 What This Book Is and Is Not 3
1.2 On Autonomy Levels 6
1.3 Embrace the Uncertainty 7
1.4 On Electric Vehicles 8
1.5 Autonomous Driving: A Short History 9

I. Technology 12

2 Algorithms: Part 1 13
2.1 Localization: Sensors 15
2.2 Route Planning: Global 20
2.3 Prediction 21
2.4 Behavior Planning and Reinforcement Learning 33
2.5 Trajectory Planning 41

3 Algorithms: Part 2 49
3.1 Environment Perception 50
3.2 "Where am I?" 60
3.3 Object Tracking 69
3.4 Route Search 72
3.5 Prediction 75
3.6 Motion Planning 77
3.7 End-to-End Autonomous Driving 80

4 Hardware Platform 85
4.1 Brief History of Automotive Chips 86
4.2 E/E Architecture Evolution 88
4.3 Computation Hardware 91
4.4 Hardware for Safety 96
4.5 Snapdragon Computation Platforms 100
4.6 Communication Hardware 102

5 Software Platform 107
5.1 Software Architecture Overview 108
5.2 System Software: Hypervisor 110
5.3 System Software: Middleware 112
5.4 System Software: OS Core 117
5.5 Functional Software 120
5.6 Application Software 122
5.7 Vehicle-Cloud Synergy 125
5.8 Case Study: Evolution of Apollo 126

6 Data Management 131
6.1 Data Ecosystem 132
6.2 Data Architectures 134
6.3 Data Governance 137
6.4 Data Management 139
6.5 Case Study: Cloud-Based Data Management 150

7 V2X: Cooperative Driving 157
7.1 DSRC and C-V2X 159
7.2 Application Scenarios 160
7.3 Global Efforts on V2X 163
7.4 Collaborative Perception 169

II. Business and Regulation 174

8 Global Market Dynamics 175
8.1 US: Fragmented Leadership 177
8.2 China: Policy-Driven Innovation at Scale 181
8.3 Europe: Regulation-Driven Advancement 184
8.4 Hybrid Models 187

9 Value Chain and Ecosystem 189
9.1 Vehicle Physical Systems 190
9.2 Vehicle Communication Infrastructure 202
9.3 Domain Controllers 206
9.4 Sensors 209
9.5 Cloud Services 211
9.6 Changing Landscape 214

10 Business Models 217
10.1 Platform-Based Model 218
10.2 Mobility-as-a-Service Model 220
10.3 Fleet Ownership Model 222
10.4 Subscription Model 224
10.5 Data Monetization Model 225
10.6 Preventing Model Degradation 227

11 Commercialization Tarpit 229
11.1 Funding Mechanisms 231
11.2 Commercialization Stages 233
11.3 Survivors 239
11.4 Commercialization Cycles 243
11.5 Commercial Alliance Structures 245

12 Industry Verticals 249
12.1 Autonomous Freight Trucking 253
12.2 Autonomous Shuttles 257
12.3 Urban Distribution 260
12.4 Special Scenarios 263
12.5 Robotaxi 266

13 Battery 273
13.1 Battery as System 27
13.2 Raw Materials 276
13.3 Cell Components 280
13.4 Manufacturing 284
13.5 Cost Reduction 288
13.6 Recycling 290
13.7 Battery Safety 295
13.8 Reflection 298

14 Safety Standards and Regulations 301
14.1 Functional Safety 303
14.2 Safety of the Intended Functionality 316
14.3 Cybersecurity 319

A Autonomous Driving Legal Frameworks of the EU and China: A Comparative Study 323
A.1 Legislative Progress Overview 323
A.2 Product Approval and Road Operation 327
A.3 Liability Attribution 330
A.4 Data Governance 336

Index 341

Über Autor(innen)

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.