Investigation of Driver Performance With Night-Vision and Pedestrian-Detection Systems—Part 2: Queuing Network Human Performance Modeling
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.
- Record URL:
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Availability:
- Find a library where document is available. Order URL: http://worldcat.org/oclc/41297384
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Supplemental Notes:
- Abstract reprinted with permission of IEEE.
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Authors:
- Tsimhoni, O
- Lim, Ji Hyoun
- Liu, Yili
- Publication Date: 2010
Language
- English
Media Info
- Media Type: Web
- Features: Figures; Photos; References;
- Pagination: pp 765-772
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Serial:
- IEEE Transactions on Intelligent Transportation Systems
- Volume: 11
- Issue Number: 4
- Publisher: Institute of Electrical and Electronics Engineers (IEEE)
- ISSN: 1524-9050
- Serial URL: http://ieeexplore.ieee.org/xpl/RecentIssue.jsp?punumber=6979
Subject/Index Terms
- TRT Terms: Behavior; Cognition; Computer models; Driver information systems; Machine learning; Night vision; Pedestrian detectors
- Subject Areas: Highways; Operations and Traffic Management; Safety and Human Factors; I83: Accidents and the Human Factor; I85: Safety Devices used in Transport Infrastructure;
Filing Info
- Accession Number: 01333664
- Record Type: Publication
- Source Agency: UC Berkeley Transportation Library
- Files: TLIB
- Created Date: Mar 21 2011 2:15PM