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

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3 Author(s)
Ji Hyoun Lim ; Dept. of Ind. & Oper. Eng., Univ. of Michigan, Ann Arbor, MI, USA ; Yili Liu ; Tsimhoni, O.

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.

Published in:

Intelligent Transportation Systems, IEEE Transactions on  (Volume:11 ,  Issue: 4 )