Investigation of driver performance with night-vision and pedestrian-detection systems-part 2: queuing network human performance modeling

Authors:
Ji Hyoun Lim;Yili Liu;Omer Tsimhoni
Affiliations:
Mobile Communication Division, Samsung Electronics, Seoul, Korea and Department of Industrial and Operations Engineering, University of Michigan, Ann Arbor, MI;Department of Industrial and Operations Engineering, University of Michigan, Ann Arbor, MI;General Motors Advanced Technical Center-Israel, Herzliya, Israel and University of Michigan Transportation Research Institute and Department of Industrial and Operations Engineering, University o ...
Venue:
IEEE Transactions on Intelligent Transportation Systems
Year:
2010

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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 eyemovement strategies generated different eye-movement behaviors, in accord with the empirical findings.