Search-Based Optimal Motion Planning for Automated Driving

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

Research output: Chapter in Book/Report/Conference proceedingConference contribution

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.
Original languageEnglish
Title of host publication 2018 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS)
PublisherInstitute of Electrical and Electronics Engineers
Pages4523-4530
Number of pages8
ISBN (Electronic)978-1-5386-8094-0
DOIs
Publication statusPublished - 2018
Event2018 IEEE/RSJ International Conference on Intelligent Robots and Systems: IROS 2018 - Madrid, Spain
Duration: 1 Oct 20185 Oct 2018

Conference

Conference2018 IEEE/RSJ International Conference on Intelligent Robots and Systems
Abbreviated titleIROS 2018
CountrySpain
CityMadrid
Period1/10/185/10/18

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