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학술지 Predicting the EEG Level of a Driver Based on Driving Information
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저자
김현숙, 윤대섭, 신현순, 박정희
발행일
201904
출처
IEEE Transactions on Intelligent Transportation Systems, v.20 no.4, pp.1215-1225
ISSN
1524-9050
출판사
IEEE
DOI
https://dx.doi.org/10.1109/TITS.2018.2848300
협약과제
18GH1100, 자율주행자동차(SAE 레벨 2,3) 기반 인적요인 심층 연구, 윤대섭
초록
© 2000-2011 IEEE. Driving is an essential activity in today's busy and complex society, and it demands physical and mental abilities, collectively known as a driving workload. For safe and comfortable driving, it would be useful to detect when drivers are being overloaded. Analyzing driver's workload using an electroencephalograph (EEG) is useful for this purpose. However, it is very inconvenient to obtain an EEG during actual driving, since the measuring device needs to be attached to the driver. In this paper, we develop a model to predict the driver's EEG level utilizing basic information obtained while the vehicle is being driven. We divided the EEG values into two classes, 'normal' and 'overload', and extracted useful features from the vehicle driving information, such as engine RPM, vehicle speed, lane changes, and turns. A classification model using a support vector machine was built to predict normal and overload states during actual driving. We evaluated the performance of the proposed method using field-of-test data collected when driving on actual roads, and suggest directions for future research based on an analysis of the experimental results.
키워드
Cognitive workload, driver behavior, driver workload, electroencephalograph, support vector machine
KSP 제안 키워드
Classification models, Cognitive workload, Data collected, Driver Behavior, Driver workload, Support VectorMachine(SVM), Test data, Vehicle driving, measuring device, vehicle speed