Investigation of driver performance with night vision and pedestrian detection systems, Part 2: Queueing network performance modeling
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- Investigation of driver performance with night vision and pedestrian detection systems, Part 2: Queueing network performance modeling
- Lim, Ji Hyoun; Omer Tsimhoni; Yili, Liu
- DETECTION PROBABILITY; VISUAL-SEARCH; CLUTTER; COGNITION; BEHAVIOR; Cognitive model; human performance modeling; night vision; pedestrian detection; queueing network
- Issue Date
- IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
- IEEE TRANSACTIONS ON INTELLIGENT TRANSPORTATION SYSTEMS, v.11, no.4, pp.706 - 714
- 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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