Driving Intelligence examines artificial intelligence through the lens of autonomous driving, using driving as a real-world test case for intelligence. At a time when expectations of artificial general intelligence are rising, the book challenges how intelligence is defined and measured, arguing that current benchmarks saturate, leak, and can be gamed.
The book develops a framework for understanding intelligence through the concept of real-world agency, with autonomous driving as its central case study. It argues that driving uniquely combines scale, open-ended environmental complexity, and continuous multi-agent interaction under uncertainty. Unlike most AI benchmarks, which are static or isolated, autonomous driving unfolds in a dynamic physical and social world that cannot be exhaustively specified in advance. The book analyzes failure modes in perception, prediction, planning, and interaction, showing how these limitations reveal a deeper gap between statistical learning from large-scale data and genuine agency. This volume, Green, traces the parallel development of AI and autonomous driving from their origins to today's frontier systems, laying the trilogy's conceptual foundations.
The book's central contribution is to reframe autonomous driving as a benchmark for intelligence grounded in real-world agency. It identifies systematic limitations in current AI approaches and clarifies what is missing for genuine autonomy, providing a concrete framework for evaluating progress in Real-World AI and Physical AI. The book will be essential reading for professionals, academics and students working and researching AI and autonomous vehicles, as well as the interested general reader.
Table of Contents:
Foreword. Preface. About the Authors. Chapter 1 Self Driving to the Future. Chapter 2 Computing Machinery & Intelligence. Chapter 3 First Steps in Computer Vision. Chapter 4 The Tortoise and the Hare. Chapter 5 The DARPA Grand Challenge for Autonomous Vehicles. Chapter 6 Forms of Machine Learning. Chapter 7 Types of Machine-Learning Models. Chapter 8 Second & Third DARPA Challenges. Chapter 9 Theory, Empiricism, and Data. Chapter 10 New Forms of Learning. Chapter 11 New Types of Model. Chapter 12 A Whole New Industry Unfolding. Chapter 13 Recurrent Sequence‑To‑Sequence Learning. Chapter 14 Attention Is All You Need. Chapter 15 Large Language Models and Multimodal Systems. Chapter 16 End‑to‑end Neural Networks. Chapter 17 A Question of Strategy. Chapter 18 Green Lights Ahead!. Bibliography. Index
About the Author :
J. Mark Bishop has served as Professor of Cognitive Computing and Director of The Centre for AI and Analytics (TCIDA), at Goldsmiths, University of London. Mark was elected Chair of the AISB (the UK professional body for AI, and the oldest such organisation in the world) from 2010-2014. He currently acts as Chief Scientific Advisor to Fact3602 and is an International Fellow of the Karel Čapek Center, Praha, Czech Republic.
Gabriel Seiberth is a distinguished automotive industry professional with 25 years of experience in management consulting and technology. He currently holds a top management position at a leading global electronics and services company for the automotive industry. Previously, he served as Managing Director at a leading global technology and consulting firm, where he authored several influential thought leadership pieces around autonomous driving.
