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Cognitive workload estimation through lateral driving performance

Title
Cognitive workload estimation through lateral driving performance
Authors
Son, J.[Son, Joon Woo]Park, S.[Son, Joon Woo]
DGIST Authors
Son, J.[Son, Joon Woo]; Park, S.[Son, Joon Woo]
Issue Date
2011
Citation
SAE Technical Papers
Type
Article
Article Type
Conference Paper
Keywords
Accuracy RateAutomobile DriversAutomobile Steering EquipmentCognitive WorkloadsDriving PerformanceEmpirical ApproachLateral ControlRadial Basis Probabilistic Neural NetworksReal-TimeStandard DeviationSteering WheelTraining and TestingWheels
ISSN
0148-7191
Abstract
This paper presents an empirical approach for estimating driver's cognitive workload using driving performance, especially lateral control ability through readily available sensors such as lane position and steering wheel angle. To develop a real-time approach for detecting cognitive distraction, radial basis probabilistic neural networks (RBPNN) were applied. Data for training and testing the RBPNN models were collected in a simulator experiment in which fifteen participants drove through a highway and were asked to complete auditory recall tasks. The best performing model could detect cognitive workload at the accuracy rate of 73.3%. The results demonstrated that the standard deviation of lane position and steering wheel reversal rate can be used to detect driver's cognitive distraction in real time. Copyright © 2011 SAE International.
URI
http://hdl.handle.net/20.500.11750/3472
DOI
10.4271/2011-28-0039
Publisher
SAE International
Related Researcher
Files:
There are no files associated with this item.
Collection:
ETC1. Journal Articles
Companion Diagnostics and Medical Technology Research Group1. Journal Articles


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