Search-Based Optimal Motion Planning for Automated Driving

Zlatan Ajanovic, Bakir Lacevic, Barys Shyrokau, Michael Stolz, Martin Horn

Publikation: Beitrag in Buch/Bericht/KonferenzbandBeitrag in einem Konferenzband


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.
Titel 2018 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS)
Herausgeber (Verlag)Institute of Electrical and Electronics Engineers
ISBN (elektronisch)978-1-5386-8094-0
PublikationsstatusVeröffentlicht - 2018
Veranstaltung2018 IEEE/RSJ International Conference on Intelligent Robots and Systems: IROS 2018 - Madrid, Spanien
Dauer: 1 Okt 20185 Okt 2018


Konferenz2018 IEEE/RSJ International Conference on Intelligent Robots and Systems
KurztitelIROS 2018

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