Safe and Rule-Aware Deep Reinforcement Learning for Autonomous Driving at Intersections

Chi Zhang, Kais Kacem, Gereon Hinz, Alois Knoll

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

2 Scopus citations

Abstract

Driving through complex urban environments is a challenging task for autonomous vehicles (AVs), as they must safely reach their mission goal, and react properly to traffic participants while obeying traffic rules. Deep reinforcement learning (DRL) is a promising method to generate driving policies for AVs because it can explore complex environments and learn suitable reactions. In this work, we present a DRL algorithm for AVs to handle intersection scenarios while considering traffic rules. Furthermore, we enhance the safety of our DRL algorithm's decisions by introducing a safety checker based on a responsibility-sensitive safety (RSS) model. Evaluations show that our DRL algorithm outperforms the baseline method by driving safely to reach the mission goal while obeying the traffic rules at an intersection.

Original languageEnglish
Title of host publication2022 IEEE 25th International Conference on Intelligent Transportation Systems, ITSC 2022
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages2708-2715
Number of pages8
ISBN (Electronic)9781665468800
DOIs
StatePublished - 2022
Event25th IEEE International Conference on Intelligent Transportation Systems, ITSC 2022 - Macau, China
Duration: 8 Oct 202212 Oct 2022

Publication series

NameIEEE Conference on Intelligent Transportation Systems, Proceedings, ITSC
Volume2022-October

Conference

Conference25th IEEE International Conference on Intelligent Transportation Systems, ITSC 2022
Country/TerritoryChina
CityMacau
Period8/10/2212/10/22

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