Journal article
Human Performance Models and Rear-End Collision Avoidance Algorithms
Human factors, Vol.43(3), pp.462-482
09/2001
DOI: 10.1518/001872001775898250
PMID: 11866201
Abstract
Collision warning systems offer a promising approach to mitigate rear-end collisions, but substantial uncertainty exists regarding the joint performance of the driver and the collision warning algorithms. A simple deterministic model of driver performance was used to examine kinematics-based and perceptual-based rear-end collision avoidance algorithms over a range of collision situations, algorithm parameters, and assumptions regarding driver performance. The results show that the assumptions concerning driver reaction times have important consequences for algorithm performance, with underestimates dramatically undermining the safety benefit of the warning. Additionally, under some circumstances, when drivers rely on the warning algorithms, larger headways can result in more severe collisions. This reflects the nonlinear interaction among the collision situation, the algorithm, and driver response that should not be attributed to the complexities of driver behavior but to the kinematics of the situation. Comparisons made with experimental data demonstrate that a simple human performance model can capture important elements of system performance and complement expensive human-in-the-loop experiments. Actual or potential applications of this research include selection of an appropriate algorithm, more accurate specification of algorithm parameters, and guidance for future experiments.
Details
- Title: Subtitle
- Human Performance Models and Rear-End Collision Avoidance Algorithms
- Creators
- Timothy L Brown - University of IowaJohn D Lee - University of IowaDaniel V McGehee - University of Iowa
- Resource Type
- Journal article
- Publication Details
- Human factors, Vol.43(3), pp.462-482
- DOI
- 10.1518/001872001775898250
- PMID
- 11866201
- NLM abbreviation
- Hum Factors
- ISSN
- 0018-7208
- eISSN
- 1547-8181
- Publisher
- SAGE Publications
- Language
- English
- Date published
- 09/2001
- Academic Unit
- Occupational and Environmental Health; Emergency Medicine; Pharmaceutical Sciences and Experimental Therapeutics; Driving Safety Research Institute; Industrial and Systems Engineering; Center for Social Science Innovation; Injury Prevention Research Center; Public Policy Center (Archive)
- Record Identifier
- 9984187059902771
Metrics
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