Due to the immense testing effort, the development and release of automated driving functions is increasingly dependent on simulations in addition to real world testing. In order to trust the simulation results, the deviations have to be quantified compared to tests on real roads. That is a prerequisite to decide if the required simulation fidelity is achieved or not. Therefore, several single quantitative and qualitative metrics are combined in this work to a multivariate scoring value for scenario-based testing in virtual environments. The approach focuses on deviations of the driven trajectories as well as the conducted maneuvers and the criticality in the traffic situations. However, the built framework for quantifying the gap compared to real world testing is generally formulated to be extendable for further features. Furthermore, scoring levels are introduced for classification. The practicable usage of the defined metric is demonstrated based on a resimulation of a scenario extracted from real driving data. The results are classified and thus the deviations are interpretable according to the used weighting factors.


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    Titel :

    Towards a Multivariate Scoring Metric for the Evaluation of Simulation Scenarios in Automated Driving


    Beteiligte:


    Erscheinungsdatum :

    2022-10-08


    Format / Umfang :

    715259 byte




    Medientyp :

    Aufsatz (Konferenz)


    Format :

    Elektronische Ressource


    Sprache :

    Englisch



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