From AI to Autonomous and Connected Vehicles

Advanced Driver-Assistance Systems (ADAS)

Inbunden, Engelska, 2021

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Beskrivning

The main topic of this book is the recent development of on-board advanced driver-assistance systems (ADAS), which we can already tell will eventually contribute to the autonomous and connected vehicles of tomorrow.With the development of automated mobility, it becomes necessary to design a series of modules which, from the data produced by on-board or remote information sources, will enable the construction of a completely automated driving system. These modules are perception, decision and action. State-of-the-art AI techniques and their potential applications in the field of autonomous vehicles are described.Perception systems, focusing on visual sensors, the decision module and the prototyping, testing and evaluation of ADAS systems are all presented for effective implementation on autonomous and connected vehicles.This book also addresses cooperative systems, such as pedestrian detection, as well as the legal issues in the use of autonomous vehicles in open environments.

Produktinformation

Utgivningsdatum:2021-10-19
Mått:10 x 10 x 10 mm
Vikt:454 g
Format:Inbunden
Språk:Engelska
Antal sidor:288
Förlag:ISTE Ltd and John Wiley & Sons Inc
ISBN:9781786307279

Utforska kategorier

Systemvetenskap och AI inom Data och IT

Mer om författaren

Abdelaziz Bensrhair is a Professor at the INSA Rouen Normandie, France. He is the Founding Manager of the pedagogical chair of excellence in autonomous and connected vehicles (INSA Rouen Normandie and the ADAS Group of the NextMove cluster) and is an expert in the automotive and mobility R&D cluster NextMove. His research in focused on the field of Intelligent Transport Systems.Thierry Bapin has a scientific and legal background and is currently deputy general manager at NextMove, the French competitiveness cluster for automotive and mobility industry. He also coordinates the ADAS Group, manages programs for the creation and development of services for NextMove members (SMEs, higher education and research institutions and local authorities) and is in charge of the sector in the Normandy region.

Innehållsförteckning

Foreword 1 xiThierry BAPINForeword 2 xiiiDominique GRUYERForeword 3 xixAlberto BROGGIPreface xxiAbdelaziz BENSRHAIRChapter 1. Artificial Intelligence for Vehicles 1Gérard YAHIAOUI1.1. What is AI? 11.2. The main methods of AI 31.2.1. Deep Learning 31.2.2. Machine Learning 41.2.3. Clustering 51.2.4. Reinforcement learning 61.2.5. Case-based reasoning 81.2.6. Logical reasoning 81.2.7. Multi-agent systems 81.2.8. PAC learning 91.3. Modern AI challenges for the industry 91.3.1. Explainability: XAI (eXplainable Artificial Intelligence) 91.3.2. The design of so-called “hybrid” AI systems 101.4. What is an “intelligent” vehicle? 101.4.1. ADAS 111.4.2. The autonomous vehicle 141.4.2. The construction of the intelligent vehicle’s basic building blocks employing AI methods 181.5. References 21Chapter 2. Conventional Vision or Not: A Selection of Low-level Algorithms 25Fabien BONARDI, Samia BOUCHAFA, Hicham HADJ-ABDELKADER and Désiré SIDIBÉ2.1. Introduction 252.2. Vision sensors 262.2.1. Conventional cameras 272.2.2. Emerging sensors 302.3. Vision algorithms 332.3.1. Choosing the type of information to be retrieved from the images 342.3.2. Estimation of ego-movement and localization 392.3.3. Detection of the navigable space by a dense approach 442.3.4. From the detection of 3D plans to visual odometry 582.3.5. Detection of obstacles through the compensation of ego-movement 622.3.6. Visual odometry 662.4. Conclusion 712.5. References 72Chapter 3. Automated Driving, a Question of Trajectory Planning 79Olivier ORFILA, Dominique GRUYER and Rémi SAINCT3.1. Definition of planning 793.2. Trajectory planning: general characteristics 813.2.1. Variables 833.2.2. Constraints 833.2.3. Cost functions 833.2.4. Planning methodology 833.2.5. Co-pilot respecting legal traffic rules 883.2.6. Trajectory prediction for “ghost” objects and vehicles 923.2.7. Trajectory evaluation 1003.2.8. Results on real vehicles and on simulators 1013.3. Multi-objective trajectory planning 1043.3.1. Linear scalarization 1073.3.2. Nonlinear scalarization 1143.3.3. Ideal methods 1163.3.4. Summary of multi-objective planning methods 1193.3.5. High level information 1193.4. Conclusion on multi-agent planning for a fleet of vehicles: the future of planning 1213.5. References 122Chapter 4. From Virtual to Real, How to Prototype, Test, Evaluate and Validate ADAS for the Automated and Connected Vehicle? 125Dominique GRUYER, Serge LAVERDURE, Jean-Sébastien BERTHY, Philippe DESOUZA and Mokrane HADJ-BACHIR4.1. Context and goals 1254.2. Generic dynamic and distributed architecture 1284.2.1. Introduction 1284.2.2. An interoperable platform 1294.3. Environment and climatic conditions 1324.3.1. Introduction 1324.3.2. Environmental modeling: lights, shadows, materials and textures 1324.3.3. Degraded, adverse and climatic conditions 1364.3.4. Visibility layers and ground truths 1404.4. Modeling of perception sensors 1434.4.1. Typology of sensor technologies 1434.4.2. From a functional model to a physical model 1454.4.3. Optical sensors 1454.4.4. LIght Detection And Ranging (LIDAR) 1494.4.5. RAdio Detection And Ranging (RADAR) 1514.4.6. Global Navigation Satellite System (GNSS) 1534.5. Connectivity and means of communication 1574.5.1. State of the art 1574.5.2. Statistical model of the propagation channel 1584.5.3. Multi-platform physico-realistic model 1594.6. Some relevant use cases 1614.6.1. Graphic resources 1614.6.2. Communication and overall risk 1614.6.3. Automated parking maneuver 1664.6.4. Co-pilot and automated driving 1694.6.5. Eco-mobility and eco-responsible driving profile 1714.7. Conclusion and perspectives 1744.8. References 176Chapter 5. Standards for Cooperative Intelligent Transport Systems (C-ITS) 181Thierry ERNST5.1. Context and goals 1825.1.1. Intelligent transport systems (ITS) 1825.1.2. The connected and cooperative vehicle 1845.1.3. Silos communication systems 1855.1.4. Cooperative Intelligent Transport Systems (C-ITS) 1865.1.5. Diversity of Cooperative ITS services 1865.1.6. Standardization bodies 1895.1.7. Genesis of the “Cooperative ITS” standards 1905.2. “ITS station” architecture 1925.2.1. General description 1925.2.2. ITS station communication units 1955.2.3. Types of ITS stations 1955.3. Features of the ITS station architecture 1975.3.1. Combination of communication technologies 1975.3.2. Centralized communications 1985.3.3. Localized communications (V2X) 1985.3.4. Hybrid communications 2005.3.5. Extensive communications 2025.3.6. Communications management 2035.3.7. Messaging 2045.3.8. Data organization and identification 2065.3.9. Secure communications and access to data 2075.3.10. Evolution of standards 2085.4. Features of the ITS station architecture 2085.5. Deployment of Cooperative ITS services 2095.6. References 213Chapter 6. The Integration of Pedestrian Orientation for the Benefit of ADAS: A Moroccan Case Study 215Aouatif AMINE, Abdelaziz BENSRHAIR, Safaa DAFRALLAH and Stéphane MOUSSET6.1. Introduction 2156.2. Advanced Driver Assistance System (ADAS) 2186.3. Proposal for an applicable system to the Moroccan case 2196.4. General conclusion 2306.5. References 231Chapter 7. Autonomous Vehicle: What Legal Issues? 233Axelle OFFROY7.1. Introduction 2337.2. The definition of the so-called “autonomous” vehicle 2347.3. Legal framework and experiments 2367.4. The notion of the “driver” 2377.5. The notion of the “custodian” 2387.6. What liability regime? 2387.7. Self-driving vehicle insurance? 2407.8. Personal data and the autonomous vehicle 2427.9. The need for uniform regulation 245List of Authors 247Index 249