Driving Intelligence: The Amber Book : Errors in Motion, or How Autonomy Fails

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, Amber, benchmarks today's autonomous driving systems against real-world performance data and tracks the shift from language-based AI toward Vision-Language-Action (VLA) systems in robotics. 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.