From AI to Autonomous and Connected Vehicles : Advanced Driver-Assistance Systems (ADAS).

Cover -- Half-Title Page -- Title Page -- Copyright Page -- Contents -- Foreword 1 -- Foreword 2 -- Foreword 3 -- Preface -- 1 Artificial Intelligence for Vehicles -- 1.1. What is AI? -- 1.2. The main methods of AI -- 1.2.1. Deep Learning -- 1.2.2. Machine Learning -- 1.2.3. Clustering -- 1.2.4. Reinforcement learning -- 1.2.5. Case-based reasoning -- 1.2.6. Logical reasoning -- 1.2.7. Multi-agent systems -- 1.2.8. PAC learning -- 1.3. Modern AI challenges for the industry -- 1.3.1. Explainability: XAI (eXplainable Artificial Intelligence) -- 1.3.2. The design of so-called "hybrid" AI systems -- 1.4. What is an "intelligent" vehicle? -- 1.4.1. ADAS -- 1.4.2. The autonomous vehicle -- 1.4.2. The construction of the intelligent vehicle's basic building blocks employing AI methods -- 1.5. References -- 2 Conventional Vision or Not: A Selection of Low-level Algorithms -- 2.1. Introduction -- 2.2. Vision sensors -- 2.2.1. Conventional cameras -- 2.2.2. Emerging sensors -- 2.3. Vision algorithms -- 2.3.1. Choosing the type of information to be retrieved from the images -- 2.3.2. Estimation of ego-movement and localization -- 2.3.3. Detection of the navigable space by a dense approach -- 2.3.4. From the detection of 3D plans to visual odometry -- 2.3.5. Detection of obstacles through the compensation of ego-movement -- 2.3.6. Visual odometry -- 2.4. Conclusion -- 2.5. References -- 3 Automated Driving, a Question of Trajectory Planning -- 3.1. Definition of planning -- 3.2. Trajectory planning: general characteristics -- 3.2.1. Variables -- 3.2.2. Constraints -- 3.2.3. Cost functions -- 3.2.4. Planning methodology -- 3.2.5. Co-pilot respecting legal traffic rules -- 3.2.6. Trajectory prediction for "ghost" objects and vehicles -- 3.2.7. Trajectory evaluation -- 3.2.8. Results on real vehicles and on simulators -- 3.3. Multi-objective trajectory planning.

3.3.1. Linear scalarization -- 3.3.2. Nonlinear scalarization -- 3.3.3. Ideal methods -- 3.3.4. Summary of multi-objective planning methods -- 3.3.5. High level information -- 3.4. Conclusion on multi-agent planning for a fleet of vehicles: the future of planning -- 3.5. References -- 4 From Virtual to Real, How to Prototype, Test, Evaluate and Validate ADAS for the Automated and Connected Vehicle? -- 4.1. Context and goals -- 4.2. Generic dynamic and distributed architecture -- 4.2.1. Introduction -- 4.2.2. An interoperable platform -- 4.3. Environment and climatic conditions -- 4.3.1. Introduction -- 4.3.2. Environmental modeling: lights, shadows, materials and textures -- 4.3.3. Degraded, adverse and climatic conditions -- 4.3.4. Visibility layers and ground truths -- 4.4. Modeling of perception sensors -- 4.4.1. Typology of sensor technologies -- 4.4.2. From a functional model to a physical model -- 4.4.3. Optical sensors -- 4.4.4. LIght Detection And Ranging (LIDAR) -- 4.4.5. RAdio Detection And Ranging (RADAR) -- 4.4.6. Global Navigation Satellite System (GNSS) -- 4.5. Connectivity and means of communication -- 4.5.1. State of the art -- 4.5.2. Statistical model of the propagation channel -- 4.5.3. Multi-platform physico-realistic model -- 4.6. Some relevant use cases -- 4.6.1. Graphic resources -- 4.6.2. Communication and overall risk -- 4.6.3. Automated parking maneuver -- 4.6.4. Co-pilot and automated driving -- 4.6.5. Eco-mobility and eco-responsible driving profile -- 4.7. Conclusion and perspectives -- 4.8. References -- 5 Standards for Cooperative Intelligent Transport Systems (C-ITS) -- 5.1. Context and goals -- 5.1.1. Intelligent transport systems (ITS) -- 5.1.2. The connected and cooperative vehicle -- 5.1.3. Silos communication systems -- 5.1.4. Cooperative Intelligent Transport Systems (C-ITS).

5.1.5. Diversity of Cooperative ITS services -- 5.1.6. Standardization bodies -- 5.1.7. Genesis of the "Cooperative ITS" standards -- 5.2. "ITS station" architecture -- 5.2.1. General description -- 5.2.2. ITS station communication units -- 5.2.3. Types of ITS stations -- 5.3. Features of the ITS station architecture -- 5.3.1. Combination of communication technologies -- 5.3.2. Centralized communications -- 5.3.3. Localized communications (V2X) -- 5.3.4. Hybrid communications -- 5.3.5. Extensive communications -- 5.3.6. Communications management -- 5.3.7. Messaging -- 5.3.8. Data organization and identification -- 5.3.9. Secure communications and access to data -- 5.3.10. Evolution of standards -- 5.4. Features of the ITS station architecture -- 5.5. Deployment of Cooperative ITS services -- 5.6. References -- 6 The Integration of Pedestrian Orientation for the Benefit of ADAS: A Moroccan Case Study -- 6.1. Introduction -- 6.2. Advanced Driver Assistance System (ADAS) -- 6.3. Proposal for an applicable system to the Moroccan case -- 6.4. General conclusion -- 6.5. References -- 7 Autonomous Vehicle: What Legal Issues? -- 7.1. Introduction -- 7.2. The definition of the so-called "autonomous" vehicle -- 7.3. Legal framework and experiments -- 7.4. The notion of the "driver" -- 7.5. The notion of the "custodian" -- 7.6. What liability regime? -- 7.7. Self-driving vehicle insurance? -- 7.8. Personal data and the autonomous vehicle -- 7.9. The need for uniform regulation -- List of Authors -- Index -- Other titles from iSTE in Mechanical Engineering and Solid Mechanics -- EULA.

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Electronic reproduction. Ann Arbor, Michigan : ProQuest Ebook Central, 2024. Available via World Wide Web. Access may be limited to ProQuest Ebook Central affiliated libraries.