Efficient Computation of Invariably Safe States for Motion Planning of Self-Driving Vehicles

Christian Pek, Matthias Althoff

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

20 Scopus citations

Abstract

Safe motion planning requires that a vehicle reaches a set of safe states at the end of the planning horizon. However, safe states of vehicles have not yet been systematically defined in the literature, nor does a computationally efficient way to obtain them for online motion planning exist. To tackle the aforementioned issues, we introduce invariably safe sets. These are regions that allow vehicles to remain safe for an infinite time horizon. We show how invariably safe sets can be computed and propose a tight under-approximation which can be obtained efficiently in linear time with respect to the number of traffic participants. We use invariably safe sets to lift safety verification from finite to infinite time horizons. In addition, our sets can be used to determine the existence of feasible evasive maneuvers and the criticality of scenarios by computing the time-to-react metric.

Original languageEnglish
Title of host publication2018 IEEE/RSJ International Conference on Intelligent Robots and Systems, IROS 2018
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages3523-3530
Number of pages8
ISBN (Electronic)9781538680940
DOIs
StatePublished - 27 Dec 2018
Event2018 IEEE/RSJ International Conference on Intelligent Robots and Systems, IROS 2018 - Madrid, Spain
Duration: 1 Oct 20185 Oct 2018

Publication series

NameIEEE International Conference on Intelligent Robots and Systems
ISSN (Print)2153-0858
ISSN (Electronic)2153-0866

Conference

Conference2018 IEEE/RSJ International Conference on Intelligent Robots and Systems, IROS 2018
Country/TerritorySpain
CityMadrid
Period1/10/185/10/18

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