Theories and Practices of Self-Driving Vehicles

Theories and Practices of Self-Driving Vehicles

1st Edition - August 1, 2022

Write a review

  • Authors: Qingguo Zhou, Zebang Shen, Binbin Yong, Rui Zhao, Peng Zhi
  • Paperback ISBN: 9780323994484

Purchase options

Purchase options
Available for Pre-Order
Sales tax will be calculated at check-out

Institutional Subscription

Free Global Shipping
No minimum order

Description

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.

Key Features

  • 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

Readership

Researchers and graduate students in robotics or automotive engineering

Table of Contents

  • 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

Product details

  • No. of pages: 300
  • Language: English
  • Copyright: © Elsevier 2022
  • Published: August 1, 2022
  • Imprint: Elsevier
  • Paperback ISBN: 9780323994484

About the Authors

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, China

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 learning

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, China

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, China

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, China

Ratings and Reviews

Write a review

There are currently no reviews for "Theories and Practices of Self-Driving Vehicles"