Computationally Efficient Fail-safe Trajectory Planning for Self-driving Vehicles Using Convex Optimization

Christian Pek, Matthias Althoff

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

44 Scopus citations

Abstract

Ensuring the safety of self-driving vehicles is a challenging task, especially if other traffic participants severely deviate from the predicted behavior. One solution is to ensure that the vehicle is able to execute a collision-free evasive trajectory at any time. However, a fast method to plan these socalled fail-safe trajectories does not yet exist. Our new approach plans fail-safe trajectories in arbitrary traffic scenarios by incorporating convex optimization techniques. By integrating safety verification in the planner, we are able to generate fail-safe trajectories in real-time, which are guaranteed to be safe. At the same time, we minimize jerk to provide enhanced comfort for passengers. The proposed benefits are demonstrated in different urban and highway scenarios using the CommonRoad benchmark suite and compared to a widely-used sampling-based planner.

Original languageEnglish
Title of host publication2018 IEEE Intelligent Transportation Systems Conference, ITSC 2018
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages1447-1454
Number of pages8
ISBN (Electronic)9781728103235
DOIs
StatePublished - 7 Dec 2018
Event21st IEEE International Conference on Intelligent Transportation Systems, ITSC 2018 - Maui, United States
Duration: 4 Nov 20187 Nov 2018

Publication series

NameIEEE Conference on Intelligent Transportation Systems, Proceedings, ITSC
Volume2018-November

Conference

Conference21st IEEE International Conference on Intelligent Transportation Systems, ITSC 2018
Country/TerritoryUnited States
CityMaui
Period4/11/187/11/18

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