Decision-making Strategies for Automated Driving in Urban Environments
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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.
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.
Nominated as an outstanding PhD thesis by Universidad Politécnica de Madrid, Spain Proposes a novel decision-making and planning architecture for autonomous vehicle navigation Describes both theoretical and experimental validations on real urban-like environments
Autor*in
Antonio Artuñedo
Themen in »Decision-making Strategies for Automated Driving in Urban Environments«
Urban Automated Driving Autonomous Driving Navigation Routing and Planning Architecture Obstacle Avoidance Motion Planning Global Planning Capabilities Local Planning Capabilities Automatic Road Corridor Generation Algorithm Vision-based Road Corridor Adaptation Algorithm Optimal Motion Planning Optimal Path Planning Risk Estimation Algorithms Inverse Perspective Mapping Motion Planning Dynamic and Uncertain Environments Trajectory Planning in Dynamic Environment