The latest advances in Deep Learning allow for increasing the automation and safety of driving systems. However, the shortage of openly available railway datasets makes it difficult to validate automated train systems. In this paper, we present a methodology to build 3D virtual railway environments for a scenario-based validation of perception systems on simulated data. The virtual environment is designed based on an ontology for railway environments following the data model of ASAM OpenLABEL. The environment is then employed to generate scenario-based synthetic data and validate perception systems in different conditions. Finally, we showcase our methodology with a practical use case focused on the validation of a rail-track detection model in different domains.


    Zugriff

    Zugriff prüfen

    Verfügbarkeit in meiner Bibliothek prüfen

    Bestellung bei Subito €


    Exportieren, teilen und zitieren



    Titel :

    Scenario-Based Validation of Automated Train Systems Using a 3D Virtual Railway Environment




    Erscheinungsdatum :

    2023-09-24


    Format / Umfang :

    6713554 byte





    Medientyp :

    Aufsatz (Konferenz)


    Format :

    Elektronische Ressource


    Sprache :

    Englisch



    Jamming Test Scenario Design for Satellite-Based Train Positioning in Railway Signaling Systems

    Xu, Kang-zhi / Liu, Jiang / Cai, Bai-gen et al. | British Library Conference Proceedings | 2022


    Jamming Test Scenario Design for Satellite-Based Train Positioning in Railway Signaling Systems

    Xu, Kang-zhi / Liu, Jiang / Cai, Bai-gen et al. | Springer Verlag | 2022



    Experimental validation of train control systems by using a railway model

    Einer, S. / Schrom, H. / Slovak, R. et al. | British Library Conference Proceedings | 2002


    Scenario Identification for Validation of Automated Driving Functions

    Elrofai, Hala / Worm, Daniël / Op den Camp, Olaf | Springer Verlag | 2016