Hazards from complex operational scenarios bring huge challenges for autonomous driving. This study proposes the External Operational Scenario-Systems Theoretic Process Analysis (EOS-STPA) approach, which extends the scope of traditional Systems Theoretic Process Analysis (STPA) to operational scenarios. By incorporating ontology-based knowledge representation and employing a hierarchical control structure that encompasses scenario control actions with feedback loops to ensure adaptive decision-making, EOS-STPA can formalize the interaction between systems and operational scenarios. Furthermore, EOS-STPA identifies and generates formalized scenario safety constraints comprehensively while enhancing safety of the intended functionality (SOTIF) for AD. Additionally, EOS-STPA’s hierarchical control modeling facilitates efficient scenario hazard identification through structured scenario decomposition. In the context of an autonomous vehicle car-following scenario, EOS-STPA demonstrates its strong capability in formalized safety analysis. This study is a pioneering effort to extend STPA to external operational scenarios while transforming technical system perspectives into operational scenario viewpoints.


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    Titel :

    Ontology-Enhanced STPA Method of Scenario Safety Constraint Identification for Autonomous Driving


    Beteiligte:
    Zhao, Mingyang (Autor:in) / Liang, Ci (Autor:in) / Ci, Yusheng (Autor:in)


    Erscheinungsdatum :

    01.10.2025




    Medientyp :

    Aufsatz (Zeitschrift)


    Format :

    Elektronische Ressource


    Sprache :

    Englisch




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