The importance of data-driven methods in automotive development continuously increases. In this area, reinforcement learning methods show great potential, but the required data from system interaction can be expensive to produce during the traditional development process. In the automotive industry, data collection is additionally constrained by privacy aspects with regard to intellectual property interests or customer data. Suitable reinforcement learning approaches need to overcome these challenges for effective and efficient learning. One possible solution is the utilization of federated learning that enables learning on distributed data through model aggregation. Therefore, we investigate the federated reinforcement learning methodology and propose a concept for a continuous automotive development process. The concept contributes separated training loops for the development and for the field operation. Furthermore, we present a customization and verification procedure within the aggregation step. The approach is exemplary shown for an electric motor current control.


    Zugriff

    Zugriff prüfen

    Verfügbarkeit in meiner Bibliothek prüfen

    Bestellung bei Subito €


    Exportieren, teilen und zitieren



    Titel :

    Data-Driven Automotive Development: Federated Reinforcement Learning for Calibration and Control


    Weitere Titelangaben:

    Proceedings


    Beteiligte:
    Bargende, Michael (Herausgeber:in) / Reuss, Hans-Christian (Herausgeber:in) / Wagner, Andreas (Herausgeber:in) / Rudolf, Thomas (Autor:in) / Schürmann, Tobias (Autor:in) / Skull, Matteo (Autor:in) / Schwab, Stefan (Autor:in) / Hohmann, Sören (Autor:in)

    Erschienen in:

    Erscheinungsdatum :

    14.03.2022


    Format / Umfang :

    16 pages





    Medientyp :

    Aufsatz/Kapitel (Buch)


    Format :

    Elektronische Ressource


    Sprache :

    Deutsch




    AVDDPG: Federated reinforcement learning applied to autonomous platoon control

    Boin, Christian / Lei, Lei / Yang, Simon X. | ArXiv | 2022

    Freier Zugriff


    Vehicles Control: Collision Avoidance using Federated Deep Reinforcement Learning

    Elallid, Badr Ben / Abouaomar, Amine / Benamar, Nabil et al. | ArXiv | 2023

    Freier Zugriff


    Personalized Federated Learning for Automotive Intrusion Detection Systems

    Shibly, Kabid Hassan / Hossain, Md Delwar / Inoue, Hiroyuki et al. | IEEE | 2022