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Decision-making Strategies for Automated Driving in Urban Environments

Antonio Artuñedo

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Springer International Publishing img Link Publisher

Naturwissenschaften, Medizin, Informatik, Technik / Elektronik, Elektrotechnik, Nachrichtentechnik

Beschreibung

This book describes an effective decision-making and planning architecture for enhancing the navigation capabilities of automated vehicles in the presence of non-detailed, open-source maps. The system involves dynamically obtaining road corridors from map information and utilizing a camera-based lane detection system to update and enhance the navigable space in order to address the issues of intrinsic uncertainty and low-fidelity. An efficient and human-like local planner then determines, within a probabilistic framework, a safe motion trajectory, ensuring the continuity of the path curvature and limiting longitudinal and lateral accelerations. LiDAR-based perception is then used to identify the driving scenario, and subsequently re-plan the trajectory, leading in some cases to adjustment of the high-level route to reach the given destination. The method has been validated through extensive theoretical and experimental analyses, which are reported here in detail.


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Schlagwörter

Optimal Path Planning, Risk Estimation Algorithms, Trajectory Planning in Dynamic Environment, Obstacle Avoidance Motion Planning, Optimal Motion Planning, Dynamic and Uncertain Environments, OSM-based Navigation, Trajectory Optimization Algorithm, LiDAR-based Perception, Global Planning Capabilities, Local Planning Capabilities, Autonomous Driving Navigation, Urban Automated Driving, Motion Planning, Self-generated Driving Corridors, Automatic Road Corridor Generation Algorithm, Routing and Planning Architecture, AUTOPIA Architecture, Inverse Perspective Mapping, Vision-based Road Corridor Adaptation Algorithm