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.


    Access

    Check access

    Check availability in my library

    Order at Subito €


    Export, share and cite



    Title :

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


    Additional title:

    Proceedings



    Published in:

    Publication date :

    2022-03-14


    Size :

    16 pages





    Type of media :

    Article/Chapter (Book)


    Type of material :

    Electronic Resource


    Language :

    German




    Vehicles Control: Collision Avoidance using Federated Deep Reinforcement Learning

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


    Communication-efficient and federated multi-agent reinforcement learning

    Krouka, M. (Mounssif) / Elgabli, A. (Anis) / Issaid, C. B. (Chaouki Ben) et al. | BASE | 2022

    Free access

    A Selective Federated Reinforcement Learning Strategy for Autonomous Driving

    Fu, Yuchuan / Li, Changle / Yu, F. Richard et al. | IEEE | 2023


    Enhancing WiFi Multiple Access Performance with Federated Deep Reinforcement Learning

    Zhang, Lyutianyang / Yin, Hao / Zhou, Zhanke et al. | IEEE | 2020


    Vehicular cooperative perception through action branching and federated reinforcement learning

    Abdel-Aziz, M. K. (Mohamed K.) / Perfecto, C. (Cristina) / Samarakoon, S. (Sumudu) et al. | BASE | 2022

    Free access