@article{ref1,
title="Investigation of Driver Performance With Night-Vision and Pedestrian-Detection Systems - Part 2: Queuing Network Human Performance Modeling",
journal="IEEE transactions on intelligent transportation systems",
year="2010",
author="Lim, Ji Hyoun and Liu, Yili and Tsimhoni, Omer",
volume="11",
number="4",
pages="765-772",
abstract="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.<p />",
language="",
issn="1524-9050",
doi="10.1109/TITS.2010.2049844",
url="http://dx.doi.org/10.1109/TITS.2010.2049844"
}