The assessment of automated driving relies increasingly on scenario-based virtual tests to achieve sufficient test coverage. Scenarios are generally based on ground truth information. Therefore, it is necessary to reproduce the view of the environment of the automated vehicle as it is seen by the autonomous driving function in the simulation. Typically, this view is erroneous compared to the ground truth due to sensor errors. This paper presents a novel approach to cluster, identify and finally to reproduce sensor errors by maneuver-dependent statistical models for the detection of other traffic objects. Errors are classified by their static and dynamic influences and incorporated into individual error models. These are evaluated in a final step based on real driving data.


    Zugriff

    Zugriff prüfen

    Verfügbarkeit in meiner Bibliothek prüfen

    Bestellung bei Subito €


    Exportieren, teilen und zitieren



    Titel :

    Modeling Perception Errors of Automated Vehicles


    Beteiligte:
    Sigl, Martin (Autor:in) / Schutz, Christoph (Autor:in) / Wagner, Sebastian (Autor:in) / Watzenig, Daniel (Autor:in)


    Erscheinungsdatum :

    2021-04-01


    Format / Umfang :

    6425723 byte





    Medientyp :

    Aufsatz (Konferenz)


    Format :

    Elektronische Ressource


    Sprache :

    Englisch



    Towards Modeling of Perception Errors in Autonomous Vehicles

    Mitra, Pallavi / Choudhury, Apratirn / Aparow, Vimal Rau et al. | IEEE | 2018


    Comparing a Linear Filter with a ML-based Approach for Modeling Perception Errors of Automated Vehicles

    Sigl, Martin / Lebherz, Andreas / Schutz, Christoph et al. | IEEE | 2022


    Toward Perception-Driven Urban Environment Modeling for Automated Road Vehicles

    Rieken, Jens / Matthaei, Richard / Maurer, Markus | IEEE | 2015


    ELEVATED PERCEPTION SYSTEM FOR AUTOMATED VEHICLES

    PROKHOROV DANIL V | Europäisches Patentamt | 2015

    Freier Zugriff

    Shared Perception for Connected and Automated Vehicles

    Kim, Yeojun / Onesto, Luca / Tay, Samuel et al. | IEEE | 2020