Reinforcement learning is nowadays a popular framework for solving different decision making problems in automated driving. However, there are still some remaining crucial challenges that need to be addressed for providing more reliable policies. In this paper, we propose a generic risk-aware DQN approach in order to learn high level actions for driving through unsignalized occluded intersections. The proposed state representation provides lane based information which allows to be used for multi-lane scenarios. Moreover, we propose a risk based reward function which punishes risky situations instead of only collision failures. Such rewarding approach helps to incorporate risk prediction into our deep Q network and learn more reliable policies which are safer in challenging situations. The efficiency of the proposed approach is compared with a DQN learned with conventional collision based rewarding scheme and also with a rule-based intersection navigation policy. Evaluation results show that the proposed approach outperforms both of these methods. It provides safer actions than collision-aware DQN approach and is less overcautious than the rule-based policy.


    Access

    Check access

    Check availability in my library

    Order at Subito €


    Export, share and cite



    Title :

    Risk-Aware High-level Decisions for Automated Driving at Occluded Intersections with Reinforcement Learning


    Contributors:


    Publication date :

    2020-10-19


    Size :

    3077882 byte





    Type of media :

    Conference paper


    Type of material :

    Electronic Resource


    Language :

    English



    RISK-AWARE HIGH-LEVEL DECISIONS FOR AUTOMATED DRIVING AT OCCLUDED INTERSECTIONS WITH REINFORCEMENT LEARNING

    Kamran, Danial / Lopez, Carlos Fernandez / Lauer, Martin et al. | British Library Conference Proceedings | 2020


    Decision Making through Occluded Intersections for Autonomous Driving

    Lin, Xiao / Zhang, Jiucai / Shang, Jin et al. | IEEE | 2019


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

    Zhang, Chi / Kacem, Kais / Hinz, Gereon et al. | IEEE | 2022


    Motion Planning for Connected Automated Vehicles at Occluded Intersections With Infrastructure Sensors

    Muller, Johannes / Strohbeck, Jan / Herrmann, Martin et al. | IEEE | 2022