Decision-making Strategies for Automated Driving in Urban Environments
Artuñedo, Antonio
Decision-making Strategies for Automated Driving in Urban Environments by Antonio Artuñedo. - First edition - 1 recurso en línea (XVIII, 195 páginas) 117 ilustraciones, 108 ilustraciones a color - Springer Theses Recognizing Outstanding Ph.D. Research 2190-5053 Intelligent Technologies and Robotics (Springer-42732) .
Introduction -- Literature Overview -- Decision Making Architecture -- Global Planning and Mapping -- Motion Prediction and Manoeuvre Planning -- Optimal Trajectory Generation -- Integration and Demonstrations.
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.
9783030459055
10.1007/978-3-030-45905-5 doi
Sistemas de asistencia a la conducción
TL272.57 / 2020 EB
Decision-making Strategies for Automated Driving in Urban Environments by Antonio Artuñedo. - First edition - 1 recurso en línea (XVIII, 195 páginas) 117 ilustraciones, 108 ilustraciones a color - Springer Theses Recognizing Outstanding Ph.D. Research 2190-5053 Intelligent Technologies and Robotics (Springer-42732) .
Introduction -- Literature Overview -- Decision Making Architecture -- Global Planning and Mapping -- Motion Prediction and Manoeuvre Planning -- Optimal Trajectory Generation -- Integration and Demonstrations.
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.
9783030459055
10.1007/978-3-030-45905-5 doi
Sistemas de asistencia a la conducción
TL272.57 / 2020 EB