The book covers basic knowledge of ROS programming, machine and deep learning, environmental perception, and sensor fusion, as well as advanced control algorithms and important areas of new research.

Format: Paperback / softback
Length: 342 pages
Publication date: 05 July 2022
Publisher: Elsevier - Health Sciences Division

Theories and Practice of Self-Driving Vehicles

Self-driving vehicles are a rapidly growing area of research and expertise, with theories and practice of self-driving vehicles presenting a comprehensive introduction to the technology. The book covers vehicle systems from principles to practice, including basic knowledge of ROS programming, machine and deep learning, and environmental perception and sensor fusion. It also introduces advanced control algorithms and important areas of new research. The book is written by two experienced authors and offers an accessible handbook to the entire stack of technology in a self-driving vehicle.

Vehicle Systems

The book begins with an introduction to vehicle systems, covering basic principles such as sensors, actuators, and control algorithms. It then moves on to cover more advanced topics such as perception, planning, and control, including topics such as object detection, path planning, and decision-making.

Perception

Perception is a critical component of self-driving vehicles, as it allows the vehicle to perceive its environment and make informed decisions. The book covers topics such as camera sensors, lidar sensors, and radar sensors, and how they are used to create a 3D map of the environment. It also covers topics such as feature extraction, object tracking, and scene understanding.

Planning

Planning is the process of creating a trajectory for the vehicle to follow. The book covers topics such as motion planning, path planning, and trajectory optimization, and how they are used to create a safe and efficient trajectory for the vehicle. It also covers topics such as traffic rules, lane changes, and intersection management.

Control

Control is the process of controlling the vehicle's motion. The book covers topics such as linear and nonlinear control, feedback control, and adaptive control, and how they are used to create a stable and responsive vehicle. It also covers topics such as energy management, safety, and reliability.

Advanced Topics

The book also covers advanced topics such as machine learning, deep learning, and neural networks, and how they are used to improve the performance of self-driving vehicles. It also covers topics such as autonomous driving, vehicle-to-vehicle communication, and urban mobility.

Conclusion

Theories and Practice of Self-Driving Vehicles is an essential resource for engineers, technicians, and students interested in self-driving vehicle technology. It offers an accessible and systematic introduction to the technology, covering vehicle systems, perception, planning, control, and advanced topics. The book is written by two experienced authors and is a valuable resource for anyone looking to learn more about self-driving vehicles.

Weight: 548g
Dimension: 151 x 227 x 21 (mm)
ISBN-13: 9780323994484