Autonomous lane changing is a critical feature for advanced autonomous driving systems, that involves several challenges such as uncertainty in other driver’s behaviors and the trade-off between safety and agility. In this work, we develop a novel simulation environment that emulates these challenges and train a deep reinforcement learning agent that yields consistent performance in a variety of dynamic and uncertain traffic scenarios. Results show that the proposed data-driven approach performs significantly better in noisy environments compared to methods that rely solely on heuristics.


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    Titel :

    Automated Lane Change Decision Making using Deep Reinforcement Learning in Dynamic and Uncertain Highway Environment


    Beteiligte:
    Alizadeh, Ali (Autor:in) / Moghadam, Majid (Autor:in) / Bicer, Yunus (Autor:in) / Ure, Nazim Kemal (Autor:in) / Yavas, Ugur (Autor:in) / Kurtulus, Can (Autor:in)


    Erscheinungsdatum :

    01.10.2019


    Format / Umfang :

    1419009 byte




    Medientyp :

    Aufsatz (Konferenz)


    Format :

    Elektronische Ressource


    Sprache :

    Englisch