Autonomous Vehicle Systems

This book offers a comprehensive, systems-level exploration of autonomous driving technology, framed through the lens of cyber-physical systems (CPS). Covering the full autonomy stack, sensing, perception, prediction, planning, control, and actuation, the book integrates computational, physical, and embedded control aspects essential for designing robust, real-time autonomous systems. With in-depth discussions on sensor modalities (LiDAR, RADAR, cameras, GNSS, IMUs), deep learning-based perception algorithms (YOLO, DETR, Mask R-CNN), multi-modal sensor fusion, and control strategies (PID, MPC, Stanley, Pure Pursuit), it bridges academic theory and industrial deployment. Practical insights into simulation tools like CARLA, drive-by-wire systems, and real-world deployment challenges make this book an essential resource for graduate students, researchers, and engineers seeking to understand and build intelligent, adaptive autonomous vehicle systems.

Comprehensive coverage of the full autonomous vehicle stack: sensing, perception, prediction, planning, control, and actuation.

In-depth analysis of sensor modalities, including LiDAR, RADAR, cameras, GNSS, and IMUs, with real-world benchmarks and datasets.

State-of-the-art deep learning algorithms for perception, including YOLO, DETR, Mask R-CNN, and multi-object tracking methods like DeepSORT and ByteTrack.

Detailed exploration of multi-modal sensor fusion strategies, addressing synchronization, redundancy, and alignment challenges.

Comparative study of control algorithms, including PID, MPC, Stanley, and Pure Pursuit, with practical tuning strategies.

Practical insights into simulation frameworks (e.g., CARLA), sensor calibration, and drive-by-wire systems using CAN protocols.

Deployment-focused content, including sensor mounting, data logging, and validation workflows. This book is for researchers and industry professionals in robotics, embedded systems, autonomous driving, and cyber-physical systems.