Theories and Practices of Self-Driving Vehicles
- 1st Edition - July 5, 2022
- Latest edition
- Authors: Qingguo Zhou, Zebang Shen, Binbin Yong, Rui Zhao, Peng Zhi
- Language: English
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
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
Researchers and graduate students in robotics or automotive engineering
1 Introduction of Self-driving vehicle system
1.1 What is self-driving vehicle
1.2 Why we need the autonomous vehicle
1.3 Basic framework of unmanned driving system
1.4 Development environment configuration
2 Overview of Robot Operating System(ROS)
2.1 Introduction to ROS
2.2 Concepts in ROS
2.3 catkin creation system
2.4 Project organization structure in ROS
2.5 Practice based on Husky simulator
2.6 Basic programming of ROS
2.7 ROS services
2.8 ROS Action
2.9 Common Tools in ROS
3 Position modules
3.1 The principle of position
3.2 Iterative Closest Point Algorithm (ICP)
3.3 Normal Distribution Transform Algorithm (NDT)
3.4 Positioning system based on GNSS-inertial integrated navigation
3.5 Slam-based position system
4 State estimation and sensor fusion
4.1 Kalman filtering and state estimation
4.2 Advanced motion model and extended Kalman filter
4.3 Unscented Kalman Filter (UKF)
5 Machine Learning and Neural Network Fundamentals
5.1 Basic Concepts of Machine Learning
5.2 Supervised learning
5.3 Fundamentals of Neural Networks
5.4 Implementing Neural Networks with Keras
6 Deep learning and visual perception
6.1 feedforward neural network?
6.2 Regularization techniques 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
7 Transfer learning and end-to-end driverless driving
7.1 Transfer learning
7.2 End-to-end driverless driving
7.3 End-to-end driverless simulation
7.4 Chapter Summary
8 Getting Started with Autonomous Driving Planning
8.1 A* Algorithm
8.2 Hierarchical Finite State Machine and Behavior Planning
8.3 Autonomous vehicle path generation based on free boundary cubic spline interpolation
8.4 motion planning method based on Frenet optimized trajectory
9 Vehicle models and advanced controls
9.1 Kinematic bicycle model and dynamic bicycle model
9.2 Getting started with unmanned vehicle control
9.3 Model predictive control based on kinematics model
9.4 Trajectory tracking
10 Reinforcement learning and its application in autonomous driving
10.1 Overview of Reinforcement Learning
10.2 Principles and Process of Reinforcement Learning
10.3 Approximate Value Function
10.4 Deep Q-value network algorithm
10.5 Strategy Gradient
10.6 Deep Deterministic Strategy Gradient and TORCS Game Control
10.7 Chapter Summary
- Edition: 1
- Latest edition
- Published: July 5, 2022
- Language: English
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