Simulation models of law-abiding pedestrians are all too prevalent. However, the models of dangerous pedestrians are frighteningly limited. This leads to the unreliability of autonomous driving in human habitats as they learn to deal with pedestrians exhaustively in a simulated world. There exists a shortage of data and analysis on dangerous pedestrians. Decision-making factors and behavior identification are not sufficient; a dangerous pedestrian model should consist of a collection of behaviors that represent their natural behavior pattern. On this basis, we propose to define these collections of behaviors in the form of pedestrian archetypes. Each archetype suggests a distinct pedestrian personality with their own special take on crossing the road, giving autonomous driving an opportunity to significantly improve their testing strategies and reliability against pedestrians.


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    Titel :

    Pedestrian Archetypes - The Must-Have Pedestrian Models for Autonomous Vehicle Safety Testing


    Beteiligte:
    Muktadir, Golam Md (Autor:in) / Huang, Taorui (Autor:in) / Bansal, Ritvik (Autor:in) / Gaidhani, Namita (Autor:in) / Jubaer, S M (Autor:in) / Lin, Michael (Autor:in) / Whitehead, Jim (Autor:in)


    Erscheinungsdatum :

    22.06.2025


    Format / Umfang :

    3898867 byte





    Medientyp :

    Aufsatz (Konferenz)


    Format :

    Elektronische Ressource


    Sprache :

    Englisch



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