Cited 4 time in webofscience Cited 5 time in scopus

Identification of Driver Cognitive Workload Using Support Vector Machines with Driving Performance, Physiology and Eye Movement in a Driving Simulator

Title
Identification of Driver Cognitive Workload Using Support Vector Machines with Driving Performance, Physiology and Eye Movement in a Driving Simulator
Authors
Son, J[Son, Joonwoo]Oh, H[Oh, Hosang]Park, M[Park, Myoungouk]
DGIST Authors
Son, J[Son, Joonwoo]; Oh, H[Oh, Hosang]; Park, M[Park, Myoungouk]
Issue Date
2013-08
Citation
International Journal of Precision Engineering and Manufacturing, 14(8), 1321-1327
Type
Article
Article Type
Article
Keywords
Cognitive WorkloadDriver State EstimationDriving SimulatorIntelligent VehicleSupport Vector Machines
ISSN
2234-7593
Abstract
This paper suggests experimental approaches for identifying driver's cognitive workload using support vector machines (SVMs) with driving performance, physiological response and eye movement data. In order to construct a classification model for detecting high cognitive workload condition, driving simulation experiments were conducted. For the experiments, 30 participants (15 younger males in the 25-35 age range (M = 27.9, SD = 3.13) and 15 older males in the 60-69 (M = 63.2, SD = 1.74)) drove a simulated highway in a fixed-base driving simulator. While driving through 37 km of straight highway, participants conducted three levels of cognitive secondary tasks, i.e. an auditory delayed digit recall task, at specified segments for 10 minutes and their driving performance, physiological response and eye movement data were collected. In this study, the model performances with different combination of measures were assessed with the nested cross-validation method. As a result, it was demonstrated that the proposed SVM models were able to identify driver's cognitive workload with high accuracy. The best performance was achieved with a combination of the standard deviation of lane position (SDLP), physiology and gaze information. The best model obtained 89.0% accuracy, sensitivity of 86.4% and specificity of 91.7%. © 2013 Korean Society for Precision Engineering and Springer-Verlag Berlin Heidelberg.
URI
http://hdl.handle.net/20.500.11750/3218
DOI
10.1007/s12541-013-0179-7
Publisher
Korean Society for Precision Engineering
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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