A comprehensive technical guide to the algorithms, architectures, and engineering behind self-driving cars.
This book provides a deep technical exploration of autonomous vehicle technology — from the raw sensor data captured by cameras and LiDAR to the control signals that steer, accelerate, and brake a car. Rather than treating self-driving cars as a black box, we dissect every layer of the autonomy stack: perception, localization, prediction, planning, and control. We also cover the recent shift toward end-to-end deep learning approaches and discuss the critical challenges that remain unsolved.
Whether you’re an engineer working in the automotive industry, a researcher in robotics or computer vision, or a technically-minded enthusiast, this book will give you a solid understanding of how autonomous vehicles actually work.
The book follows the autonomous driving pipeline from sensors to actuators, with each chapter building on the previous one. We start with the fundamentals and progressively dive deeper into the algorithms. The final chapters cover testing, open challenges, and industry trends.
This book was written in March 2026. The autonomous vehicle industry is evolving rapidly — Waymo now operates in 10+ US cities, Tesla’s FSD v12 uses a fully end-to-end neural network, and new players from China are entering the market. We have attempted to capture the state of the art as accurately as possible at the time of writing.
All code examples use Python with standard scientific computing libraries (NumPy, OpenCV, matplotlib) unless otherwise noted.