Theories and Practices of Self-Driving Vehicles

Erscheinungsjahr 2022
ISBN: 978-0-323-99448-4
Verlag: Elsevier - Health Sciences Division

Buch, Englisch, 342 Seiten, Format (B × H): 227 mm x 151 mm, Gewicht: 548 g

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.

Zielgruppe

<p>Researchers and graduate students in robotics or automotive engineering.</p>

Autoren/Hrsg.

Yong, Binbin

Zhou, Qingguo

Zhi, Peng

Shen, Zebang

Zhao, Rui

Shen, Zebang in machine learning)

Fachgebiete

• Technische Wissenschaften Elektronik | Nachrichtentechnik Elektronik Robotik
• Technische Wissenschaften Verkehrstechnik | Transportgewerbe Fahrzeugtechnik
• Technische Wissenschaften Verkehrstechnik | Transportgewerbe Intelligente & automatisierte Transportsysteme

Inhaltsverzeichnis

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

Über Autor(innen)

Zhao, Rui
Rui Zhao is a PhD student at Lanzhou University, China, who is devoted to the development of search recommendation model in the multinational company. Currently focusing on research on perception in the self-driving vehicle.

Zhi, Peng
Peng Zhi is currently pursuing his PhD degree in the School of Information Science and Engineering, Lanzhou University, China. His research interests include computer vision, deep learning, and autonomous driving

Shen, Zebang
Zebang Shen is a Senior Autonomous Driving Engineer at Daimler AG and a Google Developer Expert (GDE) in machine learning. His research focusses on the development of L4 autonomous driving systems. In particular his interests include 3D perception, multi-sensor fusion, multi-sensor automatic calibration, computer vision and 3D SLAM. He is an advocate for innovation, open source and knowledge sharing.

Yong, Binbin
Binbin Yong received his PhD degree in Computer Science and Technology from Lanzhou University in 2017. Now he is an associate professor and master's supervisor in the School of Information Science and Engineering, Lanzhou University, China. He is mainly engaged in research on high-performance computing, neural network and deep learning

Zhou, Qingguo
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.