Safety is one of the key aspects of road vehicles. With applications of machine learning and artificial intelligence (AI) technologies, driver assistance and automated driving systems have been rapidly developed. This paper identifies one of the emerging safety issues of automated driving systems: functional deficiencies resulting from limited sensing abilities and algorithmic performance. Safety validation problem and challenges for some methodologies provided by ISO 26262 are addressed. To this end, we provide a methodology framework for identifying functional deficiencies during system development. A novel methodology based on possibility theory and a fuzzy relation model, Causal Scenario Analysis (CSA), is introduced as one essential part in this framework. A traffic light handling case study is presented.


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    Titel :

    Taming Functional Deficiencies of Automated Driving Systems: a Methodology Framework toward Safety Validation


    Beteiligte:
    Chen, Meng (Autor:in) / Knapp, Andreas (Autor:in) / Pohl, Martin (Autor:in) / Dietmayer, Klaus (Autor:in)


    Erscheinungsdatum :

    01.06.2018


    Format / Umfang :

    716855 byte




    Medientyp :

    Aufsatz (Konferenz)


    Format :

    Elektronische Ressource


    Sprache :

    Englisch



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