We develop a human driver behavior model (CogMod) based on two complementary cognitive architectures; Queueing Network-Model Human Processor (QN-MHP) and Adaptive Control of Thought - Rational (ACT-R), to represent human cognition while driving. The proposed model can integrate different task-specific analytical driver models under a similar cognitive procedure. The model can simulate variable cognitive processing ability, resulting in different stopping distances in a scenario where the front vehicle brakes sharply when it enters a trigger distance. We evaluate the model based on the distribution of stopping distance with varying cognitive processing time. This approach is useful for modeling non-ego vehicles in scenario-based testing of automated vehicles (AVs).


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    Titel :

    CogMod: Simulating Human Information Processing Limitation While Driving


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


    Erscheinungsdatum :

    05.06.2022


    Format / Umfang :

    690834 byte




    Medientyp :

    Aufsatz (Konferenz)


    Format :

    Elektronische Ressource


    Sprache :

    Englisch



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