Automated vehicles are a great promise for the future of transportation systems. However, safety assurance is still a major roadblock for the mass deployment of such vehicles. This is a great challenge especially for the perception systems that have to handle a diversity of environmental conditions and road users. Up to now, perception systems treat the complete environment and all traffic participants in the same way, although only a subset of all objects has an influence on vehicle safety. To close this gap, we present in this work a comprehensive definition of safety-relevant objects, and for the most critical area around the vehicle, the safety-relevant area. Using these definitions, we demonstrate that a common object detection system is not able to detect all safety-relevant objects, which will make new, safer approaches necessary in future.


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    Titel :

    Safe Perception: On Relevance of Objects for Vehicle Safety


    Beteiligte:


    Erscheinungsdatum :

    19.09.2021


    Format / Umfang :

    1546824 byte




    Medientyp :

    Aufsatz (Konferenz)


    Format :

    Elektronische Ressource


    Sprache :

    Englisch



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