Reinforcement learning (RL) is a method which provides true learning capabilities regarding situation-based actions. RL-systems explore and self-optimise actions for situations in a defined environment. This paper describes the research of a driver (assistance) system based on pure reinforcement learning in the framework of an autonomous vehicle. The target of this research is to determine to what extent RL-based systems serve as an enhancement or even an alternative to classical concepts of autonomous intelligent vehicles such as modelling or neural nets.


    Zugriff

    Zugriff prüfen

    Verfügbarkeit in meiner Bibliothek prüfen

    Bestellung bei Subito €


    Exportieren, teilen und zitieren



    Titel :

    Optimising situation-based behaviour of autonomous vehicles


    Beteiligte:
    Krodel, M. (Autor:in) / Kuhnert, K.-D. (Autor:in)


    Erscheinungsdatum :

    01.01.2004


    Format / Umfang :

    809984 byte




    Medientyp :

    Aufsatz (Konferenz)


    Format :

    Elektronische Ressource


    Sprache :

    Englisch



    MPP1.19 Optimising Situation-Based Behaviour of Autonomous Vehicles

    Krodel, M. / Kuhnert, K.-D. / IEEE | British Library Conference Proceedings | 2004


    Towards Robust Situation Awareness in Autonomous Vehicles

    Nine, Julkar | DataCite | 2020

    Freier Zugriff

    Situation awareness for autonomous vehicles using blockchain-based service cooperation

    Nguyen, H. (Huong) / Nguyen, T. (Tri) / Leppänen, T. (Teemu) et al. | BASE | 2022

    Freier Zugriff

    Optimising Hybrid-Electric Vehicles for Europe

    Ellis, C. | British Library Conference Proceedings | 1998


    Optimising hybrid-electric vehicles for Europe

    Ellis, C.W.H. | Tema Archiv | 1998