Situational awareness is essential for safe driving, yet human drivers are often limited by their perceptual and cognitive capacities, leading to significant road accidents. Autonomous vehicles promise to eliminate humans from driving responsibility and ensure safer travel. Effective testing of autonomous vehicles, especially through scenario-based testing, is critical to their development. Accident scenarios are of particular importance in different phases of scenario-based testing. In this paper, we propose an accident scenario generation method for scenario-based testing using a human driver behavior model, CogMod, that explicitly models human perceptual and cognitive limitations. Through experimentation, we investigate the effects of perceptual and cognitive limitations on accident causation and demonstrate how this can be used in generating diverse accident scenarios.


    Zugriff

    Zugriff prüfen

    Verfügbarkeit in meiner Bibliothek prüfen

    Bestellung bei Subito €


    Exportieren, teilen und zitieren



    Titel :

    Accident Scenario Generation using Driver Behavior Model


    Beteiligte:
    Jawad, Abdul (Autor:in) / Whitehead, Jim (Autor:in)


    Erscheinungsdatum :

    24.09.2024


    Format / Umfang :

    1417266 byte





    Medientyp :

    Aufsatz (Konferenz)


    Format :

    Elektronische Ressource


    Sprache :

    Englisch



    Accident Scenario Generation with Recurrent Neural Networks

    Jenkins, Ian Rhys / Gee, Ludvig Oliver / Knauss, Alessia et al. | IEEE | 2018


    Car Driver Behavior in Railway Crossing Accident

    Inoue, T. | Online Contents | 1996


    Car Driver Behavior in Railway Crossing Accident

    Inoue, T. / Kusukami, K. / Kon-No, S. | British Library Online Contents | 1996


    Traffic and accident scenario

    Mauro,V. / Politecnico di Torino,Dip.di Energetica,IT | Kraftfahrwesen | 2005


    Statistical driver model for accident simulation

    Erbsmehl,C. / Schebdat,H. / Fraunhofer-Inst.f.Verkehrssysteme u.Infrastruktursysteme,IVI,DE | Kraftfahrwesen | 2014