| 书目名称 | Decision-making Strategies for Automated Driving in Urban Environments | | 编辑 | Antonio Artuñedo | | 视频video | | | 概述 | 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 theoretic | | 丛书名称 | Springer Theses | | 图书封面 |  | | 描述 | .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.. | | 出版日期 | Book 2020 | | 关键词 | Urban Automated Driving; Autonomous Driving Navigation; Routing and Planning Architecture; Obstacle Avo | | 版次 | 1 | | doi | https://doi.org/10.1007/978-3-030-45905-5 | | isbn_softcover | 978-3-030-45907-9 | | isbn_ebook | 978-3-030-45905-5Series ISSN 2190-5053 Series E-ISSN 2190-5061 | | issn_series | 2190-5053 | | copyright | The Editor(s) (if applicable) and The Author(s), under exclusive license to Springer Nature Switzerl |
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发表于 2025-3-21 17:07:54
