With the implementation of the new EU regulation 2022/1426 regarding the type-approval of the automated driving system (ADS) of fully automated vehicles, scenario-based testing has gained significant importance in evaluating the performance and safety of advanced driver assistance systems and automated driving systems. However, the exploration and generation of concrete scenarios from a single logical scenario can often lead to a number of similar or redundant scenarios, which may not contribute to the testing goals. This paper focuses on the the objective to reduce the scenario set by clustering concrete scenarios from a single logical scenario. By employing clustering techniques, redundant and uninteresting scenarios can be identified and eliminated, resulting in a representative scenario set. This reduction allows for a more focused and efficient testing process, enabling the allocation of resources to the most relevant and critical scenarios. Furthermore, the identified clusters can provide valuable insights into the scenario space, revealing patterns and potential problems with the system's behavior.


    Access

    Check access

    Check availability in my library

    Order at Subito €


    Export, share and cite



    Title :

    Clustering-based Criticality Analysis for Testing of Automated Driving Systems


    Contributors:
    Schutt, Barbara (author) / Otten, Stefan (author) / Sax, Eric (author)


    Publication date :

    2023-09-24


    Size :

    1905087 byte





    Type of media :

    Conference paper


    Type of material :

    Electronic Resource


    Language :

    English



    Determination of criticality for automated driving

    LINNEMANN ANDREAS / BORRMANN MARTIN / MOCK TOBIAS | European Patent Office | 2023

    Free access


    Leveraging the GIDAS Database for the Criticality Analysis of Automated Driving Systems

    Stefan Babisch / Christian Neurohr / Lukas Westhofen et al. | DOAJ | 2023

    Free access


    System Health Indicators in Mixed Criticality E/E Systems in Automated Driving Context

    Dollinger, Friederike / Asmus, Rinat / Dreiser, Marc | Fraunhofer Publica | 2020

    Free access