Sampling-Based Trajectory Repairing for Autonomous Vehicles

Yuanfei Lin, Sebastian Maierhofer, Matthias Althoff

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

11 Scopus citations

Abstract

Ensuring the safety of autonomous vehicles is a challenging task, especially if the planned trajectories do not consider all traffic rules or they are physically infeasible. Since replanning the complete trajectory is often computationally expensive, efficient methods are necessary for resolving such situations. One solution is to deform or repair an initially-planned trajectory, which we call trajectory repairing. Our approach first detects the part of an invalid trajectory that can stay unchanged. Afterward, we use a hierarchical structure and our novel sampling-based algorithm informed closed-loop rapidly-exploring random trees (informed CL-RRTs) to efficiently repair the remaining part of the trajectory. We evaluate our approach with different traffic scenarios from the CommonRoad benchmark suite. The computational efficiency is demonstrated by comparing the computation times with those required when replanning the complete trajectory.

Original languageEnglish
Title of host publication2021 IEEE International Intelligent Transportation Systems Conference, ITSC 2021
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages572-579
Number of pages8
ISBN (Electronic)9781728191423
DOIs
StatePublished - 19 Sep 2021
Event2021 IEEE International Intelligent Transportation Systems Conference, ITSC 2021 - Indianapolis, United States
Duration: 19 Sep 202122 Sep 2021

Publication series

NameIEEE Conference on Intelligent Transportation Systems, Proceedings, ITSC
Volume2021-September

Conference

Conference2021 IEEE International Intelligent Transportation Systems Conference, ITSC 2021
Country/TerritoryUnited States
CityIndianapolis
Period19/09/2122/09/21

Fingerprint

Dive into the research topics of 'Sampling-Based Trajectory Repairing for Autonomous Vehicles'. Together they form a unique fingerprint.

Cite this