Creating Autonomous Vehicle Systems

By Shaoshan Liu, Liyun Li, Jie Tang, Shuang Wu, Jean-Luc Gaudiot   

This is the first technical overview of autonomous vehicles where we share our practical experiences of creating autonomous vehicle systems. Autonomous vehicle systems are complex, consisting of three major subsystems: algorithms for localization, perception, and planning and control; client systems, such as the robotics operating system and hardware platform; and the cloud platform, which includes data storage, simulation, high-definition (HD) mapping, and deep learning model training. The algorithm subsystem extracts meaningful information from sensor raw data to understand its environment and make decisions about its actions. The client subsystem integrates these algorithms to meet real-time and reliability requirements. The cloud platform provides offline computing and storage capabilities for autonomous vehicles. Using the cloud platform, we are able to test new algorithms and update the HD map – plus, train better recognition, tracking, and decision models.

This book consists of 9 chapters: chapter 1 provides an overview of autonomous vehicle systems; chapter 2 focuses on localization technologies; chapter 3 discusses traditional techniques used for perception; chapter 4 discusses deep learning based techniques for perception; chapter 5 introduces the planning and control sub-system, especially prediction and routing technologies; chapter 6 focuses on motion planning and feedback control of the planning and control subsystem; chapter 7 introduces reinforcement learning-based planning and control; chapter 8 delves into the details of client systems design; and chapter 9 provides the details of cloud platforms for autonomous driving.

This book should be useful to students, researchers and practitioners alike. Whether you are an undergraduate or a graduate student interested in autonomous driving, you will find herein a comprehensive overview of the whole autonomous vehicle technology stack. If you are an autonomous driving practitioner, the many practical techniques introduced in this book will be of interest to you. Researchers will also find plenty of references for an effective, deeper exploration of the various technologies.

Table of Contents

1. Introduction to Autonomous Driving
2. Vehicle Localization
3. Perception in Autonomous Driving
4. Deep Learning in Autonomous Driving Perception
5. Predicting and Routing
6. Decision, Planning, and Control
7. End-to-End Planning and Control
8. Client Systems for Autonomous Driving
9. Cloud Platform for Autonomous Driving

About the Author(s)

Shaoshan Liu, PerceptIn

Liyun Li, Baidu USA

Jie Tang, South China University of Technology

Shuang Wu, YiTu

Jean-Luc Gaudiot, University of California Irvine

ISBN: 9781681730073 | 150 pages | August, 2017 | Pre-order Now


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