An important aspect of automated driving is to handle situations where it fails or is not allowed in specific traffic situations. This case study explores means, by which control transitions in a mixed autonomy system can be organized in order to minimize their adverse impact on traffic flow. We assess a number of different approaches for a coordinated management of transitions, covering classic traffic management paradigms and AI-driven controls. We demonstrate that they yield excellent results when compared to a do-nothing scenario. This text further details a model for control transitions that is the basis for the simulation study presented. The results encourage the deployment of reinforcement learning on the control problem for a scenario with mandatory take-over requests.


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    Titel :

    Reinforcement Learning-based Traffic Control: Mitigating the Adverse Impacts of Control Transitions


    Beteiligte:
    Alms, Robert (Autor:in) / Noulis, Aristeidis (Autor:in) / Mintsis, Evangelos (Autor:in) / Lücken, Leonhard (Autor:in) / Wagner, Peter (Autor:in)


    Erscheinungsdatum :

    2022-03-11



    Medientyp :

    Aufsatz (Zeitschrift)


    Format :

    Elektronische Ressource


    Sprache :

    Englisch




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