Due to the increasing complexities, the safety assurances for Automated Driving Systems (ADSs) and Advanced Driver Assistance Systems (ADASs) pose challenges. Recent development within the industry and academia suggests a scenario-based approach underpinned by the system’s Operational Design Domain (ODD) for its safety assurance. In such framework, the ODD defines the safe operating boundary, whereas the scenarios set out individual test conditions. To assess the behavior of the system, a critical element for road safety is the ability to respect the rules of the road. This paper joins together ODDs, scenarios, and rules of the road to form a scalable ODD-based safety assurance framework. The backbone of the framework contains a coherent and common taxonomy to describe the ODDs and behavior library, the scenario tagging structure from the ASAM OpenLABEL standard has been used in the example use case.The workflow utilizes the system’s ODD and behavior library as input to perform filtering and matching activities over a set of testing scenarios and the rules of the road library. Firstly, the ODD and behavior input are used to filter the applicable scenarios within the initial scenario set. At the same time the ODD and behavior input can also be used to filter the applicable rules within the rules of the road library. By further utilizing the ODD and behavior tags covered by the applicable scenarios, and applying them to the rules of the road library, the applicable scenarios related rules can be identified; similarly using the ODD and behavior tags covered by the applicable rules, and applying them to the initial scenario set, the applicable rules related scenarios can be obtained. Such process allows the most relevant rules and scenarios to be used when testing a target system. Furthermore, by comparing the applicable scenarios related rules with the applicable rules, and comparing the applicable rules related scenarios with the applicable scenarios, one can gain an understanding on the effectiveness and the efficiency of the test. When combined with the wider scenario-based evaluation criteria, the framework illustrated within this paper provides a novel and effective way to conduct and evaluate tests.


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    Titel :

    An ODD-Based Scalable Assurance Framework for Automated Driving Systems


    Weitere Titelangaben:

    Sae Technical Papers


    Beteiligte:
    Jennings, Paul (Autor:in) / Khastgir, Siddartha (Autor:in) / Zhang, Xizhe (Autor:in)

    Kongress:

    WCX SAE World Congress Experience ; 2023



    Erscheinungsdatum :

    2023-04-11




    Medientyp :

    Aufsatz (Konferenz)


    Format :

    Print


    Sprache :

    Englisch




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