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.

Provides a comprehensive introduction to the technology stack of a self-driving vehicle
Covers the three domains of perception, planning and control
Offers foundational theory and best practices
Introduces advanced control algorithms and high-potential areas of new research
Gives engineers, technicians and students an accessible handbook to self-driving vehicle technology and applications

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Yes, you can access Theories and Practices of Self-Driving Vehicles by Qingguo Zhou,Zebang Shen,Binbin Yong,Rui Zhao,Peng Zhi in PDF and/or ePUB format, as well as other popular books in Technology & Engineering & Automotive Transportation & Engineering. We have over one million books available in our catalogue for you to explore.

Information

Publisher

Elsevier

Year

2022

ISBN

9780323994491

Topic

Technology & Engineering

Subtopic

Automotive Transportation & Engineering

Index

Technology & Engineering

Cover image
Title page
Table of Contents
Copyright
Contributors
Chapter 1. First acquaintance with unmanned vehicles
Chapter 2. Introduction to robot operating system
Chapter 3. Localization for unmanned vehicle
Chapter 4. State estimation and sensor fusion
Chapter 5. Introduction of machine learning and neural networks
Chapter 6. Deep learning and visual perception
Chapter 7. Transfer learning and end-to-end self-driving
Chapter 8. Getting started with self-driving planning
Chapter 9. Vehicle model and advanced control
Chapter 10. Deep reinforcement learning and application in self-driving
Index