Self-driving vehicles are a rapidly growing area of research and expertise. Theories and Practice of Self-Driving Vehicles presents a comprehensive introduction to the technology of self driving vehicles across the three domains of perception, planning and control. The title systematically introduces vehicle systems from principles to practice, including basic knowledge of ROS programming, machine and deep learning, as well as basic modules such as environmental perception and sensor fusion. The book introduces advanced control algorithms as well as important areas of new research. This title offers engineers, technicians and students an accessible handbook to the entire stack of technology in a self-driving vehicle.
Theories and Practice of Self-Driving Vehicles presents an introduction to self-driving vehicle technology from principles to practice. Ten chapters cover the full stack of driverless technology for a self-driving vehicle. Written by two authors experienced in both industry and research, this book offers an accessible and systematic introduction to self-driving vehicle technology.
Please Note: This is an On Demand product, delivery may take up to 11 working days after payment has been received.
Theories and Practice of Self-Driving Vehicles presents an introduction to self-driving vehicle technology from principles to practice. Ten chapters cover the full stack of driverless technology for a self-driving vehicle. Written by two authors experienced in both industry and research, this book offers an accessible and systematic introduction to self-driving vehicle technology.
Please Note: This is an On Demand product, delivery may take up to 11 working days after payment has been received.
Table of Contents
1. Introduction of Self-driving vehicle system
2. Overview of Robot Operating System (ROS)
3. Position modules
4. State estimation and sensor fusion
5. Machine Learning and Neural Network Fundamentals
6. Deep learning and visual perception
7. Transfer learning and end-to-end driverless driving
8. Getting Started with Autonomous Driving Planning
9. Vehicle models and advanced controls
10. Reinforcement learning and its application in autonomous driving
