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Theories and Practices of Self-Driving Vehicles

Autor Qingguo Zhou, Zebang Shen, Binbin Yong, Rui Zhao, Peng Zhi
en Limba Engleză Paperback – 5 iul 2022
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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Specificații

ISBN-13: 9780323994484
ISBN-10: 0323994482
Pagini: 342
Dimensiuni: 152 x 229 x 21 mm
Greutate: 0.46 kg
Editura: ELSEVIER SCIENCE

Public țintă

Researchers and graduate students in robotics or automotive engineering.

Cuprins

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