Search-based optimal motion planning for automated driving

Conference Paper (2018)
Research Group
Intelligent Vehicles
Copyright
© 2018 Z. Ajanović, Bakir Lacevic, B. Shyrokau, Michael Stolz, Martin Horn
DOI related publication
https://doi.org/10.1109/IROS.2018.8593813
More Info
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Publication Year
2018
Language
English
Copyright
© 2018 Z. Ajanović, Bakir Lacevic, B. Shyrokau, Michael Stolz, Martin Horn
Research Group
Intelligent Vehicles
Pages (from-to)
4523-4530
ISBN (electronic)
978-1-5386-8094-0
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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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