Search-based optimal motion planning for automated driving

Conference paper (2018)

Authors

Z. Ajanović Intelligent Vehicles - Mechanical, Maritime and Materials Engineering , Virtual Vehicle Research Centre, Graz
Bakir Lacevic University of Sarajevo
B. Shyrokau Intelligent Vehicles - Mechanical, Maritime and Materials Engineering
Michael Stolz Virtual Vehicle Research Center
Martin Horn Graz University of Technology
Research Group
Intelligent Vehicles (Mechanical, Maritime and Materials Engineering) (TU Delft)
Copyright: © 2018 Z. Ajanović, Bakir Lacevic, B. Shyrokau, Michael Stolz, Martin Horn
DOI
help_outline
https://doi.org/10.1109/IROS.2018.8593813
Search MPC Automated driving Lane change Motion planning Traffic lights Multi-lane driving
More Info
expand_more

Published Date

2018

Language

English

Reuse Rights

Other than for strictly personal use, it is not permitted to download, forward or distribute the text or part of it, without the consent of the author(s) and/or copyright holder(s), unless the work is under an open content license such as Creative Commons.

Faculty

Mechanical, Maritime and Materials Engineering

Department

Cognitive Robotics

Research Group

Intelligent Vehicles

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.

Files

08593813.pdf
(.pdf | 1.96 Mb)