Qingguo Zhou, Zebang Shen, Binbin Yong

Theories and Practices of Self-Driving Vehicles

• Broschiertes Buch

Produktbeschreibung

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.

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Produktdetails

• Verlag: Elsevier / Elsevier Science & Technology
• Artikelnr. des Verlages: C2021-0-02037-X
• Seitenzahl: 342
• Erscheinungstermin: 5. Juli 2022
• Englisch
• Abmessung: 151mm x 227mm x 21mm
• Gewicht: 540g
• ISBN-13: 9780323994484
• ISBN-10: 0323994482
• Artikelnr.: 63240634

Autorenporträt

Qingguo Zhou is Professor at Lanzhou University and Deputy Director of the Engineering Research Center for Open Source Software and Real-Time Systems, at the Ministry of Education, China. He is also the Director of the School of Computer Science and Engineering and the Embedded System Laboratory at Lanzhou University. His research focuses on intelligent driving, AI, embedded and real-time systems. He has published widely.

Inhaltsangabe

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