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.
Investigation of Driver Performance With Night-Vision and Pedestrian-Detection Systems—Part 2: Queuing Network Human Performance Modeling
IEEE Transactions on Intelligent Transportation Systems ; 11 , 4 ; 765-772
2010-12-01
715701 byte
Article (Journal)
Electronic Resource
English
Queuing Network Modeling of Driver Workload and Performance
Online Contents | 2007
|Using Image-Based Metrics to Model Pedestrian Detection Performance With Night-Vision Systems
Online Contents | 2009
|