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.
|Title of host publication||Proceedings 2018 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS 2018)|
|Editors||Carlos Balaguer, Hajime Asama, Danica Kragic, Kevin Lynch|
|Place of Publication||Piscataway, NJ, USA|
|Publication status||Published - 2018|
|Event||2018 IEEE/RSJ International Conference on Intelligent Robots and Systems, IROS 2018 - Madrid, Spain|
Duration: 1 Oct 2018 → 5 Oct 2018
|Conference||2018 IEEE/RSJ International Conference on Intelligent Robots and Systems, IROS 2018|
|Period||1/10/18 → 5/10/18|
Bibliographical noteGreen Open Access added to TU Delft Institutional Repository 'You share, we take care!' - Taverne project https://www.openaccess.nl/en/you-share-we-take-care
Otherwise as indicated in the copyright section: the publisher is the copyright holder of this work and the author uses the Dutch legislation to make this work public.
- motion planning
- automated driving
- lane change
- traffic lights
- multi-lane driving