Abstract
This paper presents a framework for fast and robust motion planning designed to facilitate automated driving. The framework allows for real-time computation even for horizons of several hundred meters and thus enabling automated driving in urban conditions. This is achieved through several features. Firstly, a convenient geometrical representation of both the search space and driving constraints enables the use of classical path planning approach. Thus, a wide variety of constraints can be tackled simultaneously (other vehicles, traffic lights, etc.). Secondly, an exact cost-to-go map, obtained by solving a relaxed problem, is then used by A*-based algorithm with model predictive flavour in order to compute the optimal motion trajectory. The algorithm takes into account both distance and time horizons. The approach is validated within a simulation study with realistic traffic scenarios. We demonstrate the capability of the algorithm to devise plans both in fast and slow driving conditions, even when full stop is required.
Originalsprache | englisch |
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Titel | 2018 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS) |
Herausgeber (Verlag) | Institute of Electrical and Electronics Engineers |
Seiten | 4523-4530 |
Seitenumfang | 8 |
ISBN (elektronisch) | 978-1-5386-8094-0 |
DOIs | |
Publikationsstatus | Veröffentlicht - 2018 |
Veranstaltung | 2018 IEEE/RSJ International Conference on Intelligent Robots and Systems: IROS 2018 - Madrid, Spanien Dauer: 1 Okt. 2018 → 5 Okt. 2018 |
Konferenz
Konferenz | 2018 IEEE/RSJ International Conference on Intelligent Robots and Systems |
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Kurztitel | IROS 2018 |
Land/Gebiet | Spanien |
Ort | Madrid |
Zeitraum | 1/10/18 → 5/10/18 |