Automated Driving System (ADS) safety assessment is a crucial step before deployment on public roads. Despite the importance of ADS safety assurance to test ADS reliability, most of the existing work is strongly attached to a single testing data source (i.e. on-road collected testing data, simulation or test track). Each source has different fidelity levels and capabilities, therefore there is a lack of a solution that allows for all data sources to complement each other to enable agnostic end to end evaluation and contributes towards different testing goals. Evaluation of ADSs is considered as a mandatory step in the autonomous vehicle development life cycle, demanding a reliable and comprehensive method is important. Here, we propose a source-agnostic framework, which can perform ADS evaluation compatible with different testing sources. Our findings show that this comprehensive solution can save time, effort and money consumed in ADS evaluation.


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    Titel :

    A Unified Evaluation Framework for Autonomous Driving Vehicles


    Beteiligte:


    Erscheinungsdatum :

    2020-10-19


    Format / Umfang :

    2038721 byte





    Medientyp :

    Aufsatz (Konferenz)


    Format :

    Elektronische Ressource


    Sprache :

    Englisch



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