Safety requirements decomposition is critical to ensure a safe autonomous vehicle (AV) by design despite the importance of safety verification and validation. This study proposes a method called QUASARS (QUAntifying SAfety Requirements using Shapley) for efficiently decomposing AV perception safety requirements into component-level and effectively quantifying them. QUASARS models the quantification of the impact of component-level faults on system-level faults as a feature importance calculation problem. We demonstrated QUASARS using a multi-object tracking system as an example and validated component-level safety requirements 100 times on the test set. After meeting the generated component-level safety requirements, the testing system was able to meet the system-level safety requirements, indicating the effectiveness of this method in decomposing system-level safety requirements into component-level.


    Zugriff

    Zugriff prüfen

    Verfügbarkeit in meiner Bibliothek prüfen

    Bestellung bei Subito €


    Exportieren, teilen und zitieren



    Titel :

    Requirements Decomposition for Perception Systems of Autonomous Vehicles: A Case Study of Multi-Object Tracking


    Beteiligte:
    Yu, Ruilin (Autor:in) / Wang, Cheng (Autor:in) / Lv, Zhouhang (Autor:in) / Zhang, Yuxin (Autor:in)


    Erscheinungsdatum :

    24.09.2024


    Format / Umfang :

    963419 byte





    Medientyp :

    Aufsatz (Konferenz)


    Format :

    Elektronische Ressource


    Sprache :

    Englisch



    Multi-object tracking for autonomous vehicles

    RADHA HAYDER / PANG SU | Europäisches Patentamt | 2024

    Freier Zugriff

    Multi-Object Tracking For Autonomous Vehicles

    RADHA HAYDER / PANG SU | Europäisches Patentamt | 2022

    Freier Zugriff

    Lane/object detection and tracking perception system for autonomous vehicles

    CHOE TAE EUN / ZHU JUN / CHEN I-KUEI et al. | Europäisches Patentamt | 2021

    Freier Zugriff

    Environment Perception and Object Tracking for Autonomous Vehicles in a Harbor Scenario

    Lin, Jiaying / Koch, Lucas / Kurowski, Martin et al. | IEEE | 2020


    UNISOURCE MULTI-MODAL PERCEPTION IN AUTONOMOUS VEHICLES

    LI JIAN / SU HAN | Europäisches Patentamt | 2022

    Freier Zugriff