In this paper, we propose a continual learning (CL) approach that adapts to the vehicle CAN-data flexibly and continuously. Our approach is capable of learning from vehicle CAN-bus data in multiple driving scenarios, adapting to the various drifts within each driving scenario. The basis for our approach corresponds to a common solver model and a series of supervisor models. Our solver model extends the memory-aware synapses approach with the use of weight cloning and weighted experience replay. Our supervisor model selects the output of the solver model that corresponds to the driving scenario present at the input. We evaluate our approach using a Tesla Model 3 CAN-data and 8 different driving scenarios. Our evaluation results show that our approach effectively learns multiple driving scenarios sequentially without forgetting the previous knowledge.


    Zugriff

    Zugriff prüfen

    Verfügbarkeit in meiner Bibliothek prüfen

    Bestellung bei Subito €


    Exportieren, teilen und zitieren



    Titel :

    Towards Continual Knowledge Learning of Vehicle CAN-data


    Beteiligte:


    Erscheinungsdatum :

    2023-06-04


    Format / Umfang :

    1898775 byte





    Medientyp :

    Aufsatz (Konferenz)


    Format :

    Elektronische Ressource


    Sprache :

    Englisch



    Continual SLAM: Beyond Lifelong Simultaneous Localization and Mapping Through Continual Learning

    Vödisch, Niclas / Cattaneo, Daniele / Burgard, Wolfram et al. | Springer Verlag | 2023


    CONTINUAL PROACTIVE LEARNING FOR AUTONOMOUS ROBOT AGENTS

    DAVIDSON JAMES / MASON JULIAN / POURSOHI ARSHAN | Europäisches Patentamt | 2021

    Freier Zugriff

    CONTINUAL PROACTIVE LEARNING FOR AUTONOMOUS ROBOT AGENTS

    DAVIDSON JAMES / MASON JULIAN / POURSOHI ARSHAN | Europäisches Patentamt | 2022

    Freier Zugriff

    Evaluating Differential Privacy in Federated Continual Learning

    Ouyang, Junyan / Han, Rui / Liu, Chi Harold | IEEE | 2023


    CONTINUAL PROACTIVE LEARNING FOR AUTONOMOUS ROBOT AGENTS

    DAVIDSON JAMES / MASON JULIAN / POURSOHI ARSHAN | Europäisches Patentamt | 2022

    Freier Zugriff