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arXiv · 1803.04868

Search-based optimal motion planning for automated driving

Zlatan Ajanovic·Bakir Lacevic·Barys Shyrokau·Michael Stolz·Martin Horn
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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.

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Zlatan Ajanovic, Bakir Lacevic, Barys Shyrokau, Michael Stolz, Martin Horn. 2018-08-03. Search-based optimal motion planning for automated driving. https://doi.org/10.1109/iros.2018.8593813

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