Intro
Foreword
Contents
List of Figures
List of Tables
Chapter 1: Challenges of Autonomous Driving Systems
1.1 Autonomous Driving
1.1.1 Current Autonomous Driving Technology
1.2 Autonomous Driving System Challenges
1.2.1 Functional Constraints
1.2.2 Predictability Constraints
1.2.3 Storage Limitations
1.2.4 Thermal Constraints
1.2.5 Power Is Constrained
1.3 Designing an Autonomous Driving System
1.3.1 Perception Systems
1.3.2 Decision-Making
1.3.3 Vehicle Control
1.3.4 Safety Verification and Testing

1.4 The Autonomous Driving System Computing Platform
1.4.1 GPU
1.4.2 DSP
1.4.3 Field Programmable Gate Array FPGA
1.4.4 Specific Integrated Circuit ASIC
1.5 The Content of This Book
1.5.1 3D Object Detection
1.5.2 Lane Detection
1.5.3 Motion Planning and Control
1.5.4 The Localization and Mapping
1.5.5 The Autonomous Driving Simulator
1.5.6 Autonomous Driving ASICs
1.5.7 Deep Learning Model Optimization
1.5.8 Design of Deep Learning Hardware
1.5.9 Self-Driving ASICs Design
1.5.10 Operating Systems for Autonomous Driving

1.5.11 Autonomous Driving Software Architecture
1.5.12 5G C-V2X
References
Chapter 2: 3D Object Detection
2.1 Introduction
2.2 Sensors
2.2.1 Camera
2.2.2 LiDAR
2.2.3 Camera + Lidar
2.3 Datasets
2.4 3D Object Detection Methods
2.4.1 Monocular Images Based on Methods
2.4.2 Point Cloud-Based Detection Methods
2.4.2.1 Projection Methods
2.4.2.2 Volumetric Convolution Methods
2.4.2.3 Point Net Method
2.4.3 Fusion-Based Methods
2.5 Complex-YOLO: A Euler-Region-Proposal for Real-Time 3D Object Detection on Point Clouds [31]
2.5.1 Algorithm Overview

2.5.2 Point Cloud Preprocessing
2.5.3 The Proposed Architecture
2.5.4 Anchor Box Design
2.5.5 Complex Angle Regression
2.5.6 Evaluation on KITTI
2.5.7 Training
2.5.8 Birdś Eye View Detection
2.5.9 3D Object Detection
2.6 Future Research Direction
References
Chapter 3: Lane Detection
3.1 Traditional Image Processing
3.2 Example: Lane Detection Based on the Hough Transform
3.2.1 Hough Transform
3.2.2 Lane Detection
3.3 Example: RANSAC Algorithm and Fitting Straight Line
3.3.1 Overview of the RANSAC Algorithm

3.3.2 Use Python to Implement Line Fitting
3.4 Based on Deep Learning
3.5 The Multi-Sensor Integration Scheme
3.6 Lane Detection Evaluation Criteria
3.6.1 Lane Detection System Factors
3.6.2 Offline Evaluation
3.6.3 Online Evaluation
3.6.4 Evaluation Metrics
3.7 Example: Lane Detection
3.7.1 Overview
3.7.2 Loss Function
3.7.3 Experimental Results
3.7.4 Conclusion
References
Chapter 4: Motion Planning and Control
4.1 Overview
4.2 Traditional Planning and Control Solutions
4.2.1 Route Planning