In this paper, we present a safe deep reinforcement learning system for automated driving. The proposed framework leverages merits of both rule-based and learning-based approaches for safety assurance. Our safety system consists of two modules namely handcrafted safety and dynamically-learned safety. The handcrafted safety module is a heuristic safety rule based on common driving practice that ensure a minimum relative gap to a traffic vehicle. On the other hand, the dynamically-learned safety module is a data-driven safety rule that learns safety patterns from driving data. Specifically, the dynamically-leaned safety module incorporates a model lookahead beyond the immediate reward of reinforcement learning to predict safety longer into the future. If one of the future states leads to a near-miss or collision, then a negative reward will be assigned to the reward function to avoid collision and accelerate the learning process. We demonstrate the capability of the proposed framework in a simulation environment with varying traffic density. Our results show the superior capabilities of the policy enhanced with dynamically-learned safety module.


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    Titel :

    Deep Reinforcement Learning with Enhanced Safety for Autonomous Highway Driving


    Beteiligte:
    Baheri, Ali (Autor:in) / Nageshrao, Subramanya (Autor:in) / Tseng, H. Eric (Autor:in) / Kolmanovsky, Ilya (Autor:in) / Girard, Anouck (Autor:in) / Filev, Dimitar (Autor:in)


    Erscheinungsdatum :

    19.10.2020


    Format / Umfang :

    382651 byte





    Medientyp :

    Aufsatz (Konferenz)


    Format :

    Elektronische Ressource


    Sprache :

    Englisch



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