Citation

Lim JH, Liu Y, Tsimhoni O. IEEE Trans. Intel. Transp. Syst. 2010; 11(4): 765-772.

Copyright

(Copyright © 2010, IEEE (Institute of Electrical and Electronics Engineers))

DOI

10.1109/TITS.2010.2049844

PMID

unavailable

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.