Theories and Practices of Self-Driving Vehicles
- 1st Edition - July 3, 2022
- Imprint: Elsevier
- Authors: Qingguo Zhou, Zebang Shen, Binbin Yong, Rui Zhao, Peng Zhi
- Language: English
- Paperback ISBN:9 7 8 - 0 - 3 2 3 - 9 9 4 4 8 - 4
- eBook ISBN:9 7 8 - 0 - 3 2 3 - 9 9 4 4 9 - 1
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 technolog… Read more
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Request a sales quoteSelf-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
Researchers and graduate students in robotics or automotive engineering
- Cover image
- Title page
- Table of Contents
- Copyright
- Contributors
- Chapter 1. First acquaintance with unmanned vehicles
- 1.1. What are unmanned vehicles?
- 1.2. Why do we need unmanned vehicles?
- 1.3. Basic framework of the unmanned vehicle system
- 1.4. Development environment configuration
- Chapter 2. Introduction to robot operating system
- 2.1. ROS introduction
- Chapter 3. Localization for unmanned vehicle
- 3.1. Principle of achieving localization
- 3.2. ICP algorithm
- 3.3. Normal distribution transform
- 3.4. Localization system based on global positioning system (GPS) + inertial navigation system (INS)
- 3.5. SLAM-based localization system
- Chapter 4. State estimation and sensor fusion
- 4.1. Kalman filter and state estimation
- 4.2. Advanced motion modeling and EKF
- 4.3. UKF
- Chapter 5. Introduction of machine learning and neural networks
- 5.1. Basic concepts of machine learning
- 5.2. Supervised learning
- 5.3. Fundamentals of neural network
- 5.4. Using Keras to implement the neural network
- Chapter 6. Deep learning and visual perception
- 6.1. Deep feedforward neural networks—why is it necessary to be deep?
- 6.2. Regularization technology applied to deep neural networks
- 6.3. Actual combat—traffic sign recognition
- 6.4. Introduction to convolutional neural networks
- 6.5. Vehicle detection based on YOLO2
- Chapter 7. Transfer learning and end-to-end self-driving
- 7.1. Transfer learning
- 7.2. End-to-end selfdriving
- 7.3. End-to-end selfdriving simulation
- 7.4. Summary of this chapter
- Chapter 8. Getting started with self-driving planning
- 8.1. A∗ algorithm
- 8.2. Hierarchical finite state machine (HFSM) and autonomous vehicle behavior planning
- 8.3. Autonomous vehicle route generation based on free boundary cubic spline interpolation
- 8.4. Motion planning method of the autonomous vehicle based on Frenet optimization trajectory
- Chapter 9. Vehicle model and advanced control
- 9.1. Kinematic bicycle model and dynamic bicycle model
- 9.2. Rudiments of autonomous vehicle control
- 9.3. MPC based on kinematic model
- 9.4. Trajectory tracking
- Chapter 10. Deep reinforcement learning and application in self-driving
- 10.1. Overview of reinforcement learning
- 10.2. Reinforcement learning
- 10.3. Approximate value function
- 10.4. Deep Q network algorithm
- 10.5. Policy gradient
- 10.6. Deep deterministic policy gradient and TORCS game control
- 10.7. Summary
- Index
- Edition: 1
- Published: July 3, 2022
- Imprint: Elsevier
- No. of pages: 342
- Language: English
- Paperback ISBN: 9780323994484
- eBook ISBN: 9780323994491
QZ
Qingguo Zhou
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.
Affiliations and expertise
Professor, Lanzhou University and Deputy Director, Engineering Research Center for Open Source Software and Real-Time Systems, Ministry of Education, ChinaZS
Zebang Shen
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.
Affiliations and expertise
Senior Autonomous Driving Engineer, Daimler AG and Google Developer Expert (GDE) in machine learningBY
Binbin Yong
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
Affiliations and expertise
an associate professor and master's supervisor in the School of Information Science and Engineering, Lanzhou University, ChinaRZ
Rui Zhao
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.
Affiliations and expertise
is a PhD student at Lanzhou University, ChinaPZ
Peng Zhi
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
Affiliations and expertise
PhD student, School of Information Science and Engineering, Lanzhou University, ChinaRead Theories and Practices of Self-Driving Vehicles on ScienceDirect