This paper introduces a queueing network-based computational model to explain driver performance in a pedestrian-detection task assisted with night-vision-enhancement systems. The computational cognitive model simulated the pedestrian-detection task using images displayed by two night-vision systems as input stimuli. The system equipped with a far-infrared (FIR) sensor generated less-cluttered images than the system equipped with a near-infrared (NIR) sensor. Using a reinforcement learning process, the model developed eye-movement strategies for each night-vision system. The differences in eye-movement strategies generated different eye-movement behaviors, in accord with the empirical findings.


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    Title :

    Investigation of Driver Performance With Night-Vision and Pedestrian-Detection Systems—Part 2: Queuing Network Human Performance Modeling


    Contributors:
    Ji Hyoun Lim, (author) / Yili Liu, (author) / Tsimhoni, Omer (author)


    Publication date :

    2010-12-01


    Size :

    715701 byte




    Type of media :

    Article (Journal)


    Type of material :

    Electronic Resource


    Language :

    English