Deep reinforcement learning (RL) has been widely applied to motion planning problems of autonomous vehicles in urban traffic. However, traditional deep RL algorithms cannot ensure safe trajectories throughout training and deployment. We propose a provably safe RL algorithm for urban autonomous driving to address this. We add a novel safety layer to the RL process to verify the safety of high-level actions before they are performed. Our safety layer is based on invariably safe braking sets to constrain actions for safe lane changing and safe intersection crossing. We introduce a generalized discrete high-level action space, which can represent all high-level intersection driving maneuvers and various desired accelerations. Finally, we conducted extensive experiments on the inD dataset containing urban driving scenarios. Our analysis demonstrates that the safe agent never causes a collision and that the safety layer's lane changing verification can even improve the goal-reaching performance compared to the unsafe baseline agent.


    Access

    Check access

    Check availability in my library

    Order at Subito €


    Export, share and cite



    Title :

    Safe Reinforcement Learning for Urban Driving using Invariably Safe Braking Sets


    Contributors:


    Publication date :

    2022-10-08


    Size :

    905660 byte




    Type of media :

    Conference paper


    Type of material :

    Electronic Resource


    Language :

    English



    Provably-Safe Cooperative Driving via Invariably Safe Sets

    Liu, Edmond Irani / Pek, Christian / Althoff, Matthias | IEEE | 2020


    Routing using Safe Reinforcement Learning

    Nayak Seetanadi, Gautham / Årzén, Karl-Erik | BASE | 2020

    Free access

    Routing Using Safe Reinforcement Learning

    Nayak Seetanadi, Gautham | BASE | 2020

    Free access

    Safe Deep Reinforcement Learning for Adaptive Cruise Control by Imposing State-Specific Safe Sets

    Brosowsky, Mathis / Keck, Florian / Ketterer, Jakob et al. | IEEE | 2021


    Safe Braking Distance Algorithm

    International Association of Railway Operating Officers | British Library Conference Proceedings | 1998