Highlights Runtime risk evaluation of autonomous trains with monitored scenario parameters. Scenario reference model to identify key risk contributors based on the ODD. Consequence reasoning with FDBN to tackle the uncertainty of impact. The validity of the method is tested by a real accident case.

    Abstract Recent years witness the focus of the research of next-generation railways on risk situation awareness and safety decision-making to enhance the autonomy of unmanned trains. However, complex environmental factors make it difficult to assess the risks of train operation. Thus, it is of great necessity to clearly monitor the scenario parameters under which the train control system is designed to work, and to infer real-time risk through the collected scenario data. This paper first clarifies the key scenario parameters that need to be collected during the operation according to the concept of Operational Design Domain (ODD) and operating scenario. The key parameters and their dependencies are used to derive the Dynamic Bayesian Network (DBN) structure. Second, for data probability uncertainty, Fuzzy Set Theory is introduced, within the framework of which a fuzzy dynamic reasoning process is presented by monitoring the scenario data deviation. Finally, a case of real-time risk evaluation and analysis of the accident of Singapore MTR is explicated to demonstrate its contribution to operating data-based runtime risk analysis.


    Zugriff

    Zugriff prüfen

    Verfügbarkeit in meiner Bibliothek prüfen

    Bestellung bei Subito €


    Exportieren, teilen und zitieren



    Titel :

    Research on run-time risk evaluation method based on operating scenario data for autonomous train


    Beteiligte:
    Niu, Ru (Autor:in) / You, Sifan (Autor:in)


    Erscheinungsdatum :

    2022-09-25




    Medientyp :

    Aufsatz (Zeitschrift)


    Format :

    Elektronische Ressource


    Sprache :

    Englisch




    Scenario-Based Risk Quantification Approach for Assuring Safety in Autonomous Vehicles

    Madala, Kaushik / Solmaz, Mert | SAE Technical Papers | 2023


    Scenario-Based Risk Quantification Approach for Assuring Safety in Autonomous Vehicles

    Madala, Kaushik / Solmaz, Mert | British Library Conference Proceedings | 2023


    Time-domain emergency scenario analysis in rail passenger train

    Berger, Maxime / Lavertu, Carl / Kocar, Ilhan et al. | IEEE | 2016


    SIMULATION SCENARIO GENERATION BASED ON AUTONOMOUS VEHICLE DRIVING DATA

    LEE RITCHIE | Europäisches Patentamt | 2024

    Freier Zugriff

    Train-Centric Communication Based Autonomous Train Control System

    Song, Haifeng / Gao, Shigen / Li, Yidong et al. | IEEE | 2023