Format:
xv, 232 Seiten
,
Illustrationen, Diagramme
ISBN:
9789819932795
Content:
Although sensor fusion is an essential prerequisite for autonomous driving, it entails a number of challenges and potential risks. For example, the commonly used deep fusion networks are lacking in interpretability and robustness. To address these fundamental issues, this book introduces the mechanism of deep fusion models from the perspective of uncertainty and models the initial risks in order to create a robust fusion architecture. This book reviews the multi-sensor data fusion methods applied in autonomous driving, and the main body is divided into three parts: Basic, Method, and Advance. Starting from the mechanism of data fusion, it comprehensively reviews the development of automatic perception technology and data fusion technology, and gives a comprehensive overview of various perception tasks based on multimodal data fusion. The book then proposes a series of innovative algorithms for various autonomous driving perception tasks, to effectively improve the accuracy and robustness of autonomous driving-related tasks, and provide ideas for solving the challenges in multi-sensor fusion methods. Furthermore, to transition from technical research to intelligent connected collaboration applications, it proposes a series of exploratory contents such as practical fusion datasets, vehicle-road collaboration, and fusion mechanisms. In contrast to the existing literature on data fusion and autonomous driving, this book focuses more on the deep fusion method for perception-related tasks, emphasizes the theoretical explanation of the fusion method, and fully considers the relevant scenarios in engineering practice. Helping readers acquire an in-depth understanding of fusion methods and theories in autonomous driving, it can be used as a textbook for graduate students and scholars in related fields or as a reference guide for engineers who wish to apply deep fusion methods
Note:
Literaturangaben
,
Chapter 1: Introduction 1.1 Autonomous driving 1.2 Sensors 1.3 Perception 1.4 Multi-sensor fusion 1.5 Public datasets 1.5 Challenges 1.6 Summary Chapter 2: Overview of Data Fusion Theory and Methods 2.1 Background 2.2 Data pre-processing 2.3 Model-based fusion 2.4 Learning-based fusion 2.5 Challenges and prospect 2.6 Summary Chapter 3: Uncertainty in Fusion Networks 3.1 Formulate uncertainty in multimodal fusion 3.2 Model uncertainty in fusion 3.3 Data uncertainty in fusion 3.4 Redundancy and stability analysis 3.5 Challenges and prospect 3.6 Summary Chapter 4: Generalized Fusion Methods 4.1 Background 4.2 Human-machine interactive fusion 4.3 Vehicle-road multi-view interactive fusion 4.4 Challenges and prospect 4.5 Summary Chapter 5: Multi-sensor calibration and Localization 5.1 Background 5.2 Dataset and criterion 5.3 Multi-sensor fusion calibration 5.4 Multi-sensor fusion localization 5.5 Challenges and prospect 5.6 Summary Chapter 6: Multi-sensor object detection 6.1 Background 6.2 Dataset and criterion 6.3 LiDAR-Image fusion object detection 6.4 Radar-LiDAR fusion object detection 6.5 Radar-Image fusion object detection 6.6 Lightweight fusion paradigm 6.7 Challenges and prospect 6.8 Summary Chapter 7: Multi-sensor scene segmentation 7.1 Background 7.2 Dataset and criterion 7.3 Comparison of different fusion architecture in segmentation 7.4 Attention in multimodal fusion segmentation 7.5 Adaptive strategies in multimodal fusion segmentation 7.6 Challenges and prospect 7.7 Summary Chapter 8: Multi-sensor fusion for three-dimensional transportation 8.1 The scene of three-dimensional transportation 8.2 Sequential model fused with motion information 8.3 Object detection based on stereoscopic motion view 8.4 Scene segmentation based on stereoscopic motion view 8.5 Challenges and prospect 8.6 Summary Chapter 9: Platform for autonomous driving 9.1 Self-driving car 9.2 Simulation platform 9.3 Sensor evaluation and comparison 9.4 Creating datasets Chapter 10: Conclusions 10.1 Summary 10.2 Future work
Additional Edition:
9789819932801
Language:
English
Keywords:
Autonomes Fahrzeug
;
Virtueller Sensor
