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Full Description
The development of autonomous vehicles represents a major technological breakthrough, revolutionizing mobility through the integration of advanced technologies at the intersection of artificial intelligence (AI), mechatronics and control sciences. This heralds a new era of disruptive innovation for the automotive and aerospace industries.
Autonomous Vehicles offers an in-depth, structured analysis of the scientific and technical challenges associated with autonomy, while also addressing the industrial, societal, legal and safety issues specific to the automotive and aerospace sectors. This book presents the key functions of automated driving and piloting, sensor technologies and their role in environmental perception, as well as data processing and exploitation strategies based on conventional algorithmic approaches and techniques derived from AI.
This detailed book offers concrete examples from industrial use cases, offering a rigorous and up-to-date overview of a rapidly changing field, grounded in close collaboration between academia and industry.
Contents
Foreword vii
Bruno EVEN
Preface xi
Introduction xvii
Chapter 1. Vehicle Architecture 1
1.1. Generalities and levels of autonomy 1
1.2. Evolution of features associated with autonomy 3
1.2.1. Features and technologies for the automobile 3
1.2.2. Features and technologies for aeronautics 5
1.3. Vehicle architecture and control chain 7
1.3.1. Case of aeronautics 7
1.3.2. Automobile case 37
1.4. Vehicle architecture review 40
Chapter 2. Features and Autonomy 41
2.1. Simple or comfort control features 42
2.1.1. Features related to land vehicles 42
2.1.2. Features related to airborne systems: helicopter pilot assistance 47
2.2. Complex features specific to a phase of use 52
2.2.1. Automobile: parking assistance 52
2.2.2. Aeronautics: landing and parking assistance 52
2.3. Complex traffic and security features 70
2.3.1. Detection of other vehicles and traffic management 70
2.3.2. Obstacle detection 78
Chapter 3. Sensors and Technologies 81
3.1. Introduction 81
3.1.1. Proprioceptive sensors 84
3.1.2. Exteroceptive sensors 90
3.2. Sensor review and comparison 119
Chapter 4. Data Mining and Fusion 121
4.1. Introduction to the concept of data fusion 121
4.1.1. Types of data fusion 123
4.1.2. The steps of data fusion 124
4.1.3. Data fusion level 125
4.1.4. Introduction to the Kalman filter 127
4.2. DTMO: detection and tracking of moving objects 130
4.2.1. Automobile examples 130
4.2.2. Examples in aeronautics 134
4.3. SLAM: mapping and localization 149
4.3.1. Methods for identifying the vehicle environment 150
4.3.2. Communications between vehicles 152
4.3.3. Example of landing automation: EAGLE project 154
Conclusion 159
References 161
Index 167
