Intro
Foreword
Preface
Acknowledgments
Contents
Part I Basic
1 Introduction
1.1 Autonomous Driving
1.2 Sensors
1.3 Perception
1.4 Multi-Sensor Fusion
1.5 Public Datasets
1.6 Challenges
1.7 Summary
References
2 Overview of Data Fusion in Autonomous Driving Perception
2.1 A Brief Review of Deep Learning
2.2 Fusion in Depth Completion
2.3 Fusion in Dynamic Object Detection
2.4 Fusion in Stationary Road Object Detection
2.5 Fusion in Semantic Segmentation
2.6 Fusion in Object Tracking
2.7 Summary
References
Part II Method

3 Multi-Sensor Calibration
3.1 Introduction
3.2 Line-Based Multi-Sensor Calibration
3.2.1 Methodology
3.2.2 Experiment
3.3 Challenges and Prospect
3.4 Summary
References
4 Multi-Sensor Object Detection
4.1 Introduction
4.2 LiDAR-Image Fusion Object Detection
4.2.1 RI-Fusion Framework
4.2.1.1 Data Preprocessing
4.2.1.2 RI-Attention Network
4.2.1.3 Point Cloud Recovery
4.2.2 Experiment
4.2.2.1 Dataset and Evaluation Metrics
4.2.2.2 Implementation Details
4.2.2.3 Results
4.2.2.4 Ablation Studies
4.3 RaDAR-LiDAR Fusion Object Detection

4.3.1 Preprocessing of 4D RaDAR Point Clouds
4.3.2 Interaction-Based Multimodal Fusion (IMMF)
4.3.3 Center-Based Multi-Scale Fusion (CMSF)
4.3.4 Experiments
4.3.4.1 Dataset
4.3.4.2 Implementation Details
4.3.4.3 Training
4.3.4.4 3D Object Detection on Astyx HiRes 2019 Dataset
4.3.4.5 Ablation Studies with M2-Fusion
4.3.4.6 Accuracy Comparison Experiments at Different Ranges
4.3.4.7 Parameter Comparison Experiment
4.3.4.8 Visualization Experiments
4.4 Challenges and Prospect
4.5 Summary
References
5 Multi-Sensor Scene Segmentation
5.1 Background

5.2 Introduction
5.3 Attention in Multimodal Fusion Segmentation
5.3.1 Network Architectures
5.3.2 Experiments and Discussion
5.4 Adaptive Strategies in Multimodal Fusion Segmentation
5.4.1 MIMF Network
5.4.2 Experiment
5.5 Video Multimodal Fusion Segmentation
5.5.1 Method
5.5.2 Experiments
5.6 Summary
5.7 Challenges and Prospect
References
6 Multi-Sensor Fusion Localization
6.1 Introduction
6.2 GF-SLAM
6.2.1 Methodology
6.2.2 Experiment
6.3 Lifelong Localization in Semi-Dynamic Environment
6.3.1 Methodology
6.3.2 Experiment

6.4 Challenges and Prospect
6.5 Summary
References
Part III Advance
7 OpenMPD: An Open Multimodal Perception Dataset
7.1 Introduction
7.2 Automated Driving-Related Datasets
7.2.1 Comprehensive Datasets
7.2.2 Characteristic Datasets
7.2.3 Our Dataset
7.3 OpenMPD
7.3.1 Platform Setup
7.3.2 Calibration
7.3.3 Collecting Route
7.3.4 Combine Annotation
7.4 Data Analysis
7.4.1 Complexity
7.4.2 Occlusion
7.4.3 Scale
7.4.4 Position
7.5 Experiment
7.5.1 Object Detection
7.5.2 Semantic Segmentation
7.6 Summary
References