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학술지 Driver Workload Characteristics Analysis Using EEG Data From an Urban Road
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저자
김현숙, 황윤숙, 윤대섭, 최원근, 박정희
발행일
201408
출처
IEEE Transactions on Intelligent Transportation Systems, v.15 no.4, pp.1844-1849
ISSN
1524-9050
출판사
IEEE
DOI
https://dx.doi.org/10.1109/TITS.2014.2333750
협약과제
13VC4200, 운전부하 정량화 및 지능형 인터페이스 관리 기술 개발, 윤대섭
초록
The main cause of traffic accidents is drivers' human errors such as cognitive, judgment, and execution errors. To mitigate drivers' human errors, research on the measurement and quantification of driver workload as well as the development of smart vehicles is needed. Drivers' behavior while driving includes driving straight, turning left or right, U-turns, rapid acceleration, rapid deceleration, and changing lanes. To measure and quantify a driving workload, both the subjective workload and the behavior workload caused by varied driving behaviors should be taken into account on the basis of understanding the visual, auditory, cognitive, and psychomotor characteristics of the driving workload. In this paper, we analyze electroencephalogram (EEG) data collected through an urban road driving test. To overcome large deviations of EEG values among drivers, we used EEG variation rates instead of raw EEG values. We extracted five kinds of behavior sections from the data: left-turn section, right-turn section, rapid-acceleration section, rapid-deceleration section, and lane-change section. We then selected a reference section for each of these behavior sections and compared EEG values from the behavior sections with those from the reference sections to calculate the EEG variation rates, after which we made the statistical analysis. The analysis results of this study are being used to explain the cognitive characteristics of a driving workload caused by drivers' behavior in the vehicle information system, which will provide information for safe driving by taking into account the driving workload. © 2014 IEEE.
키워드
Driver behavior, driver workload, electroencephalogram (EEG), human-vehicle interface, intersection turn, lane change, rapid acceleration, rapid deceleration, subjective workload, workload management system (WMS)
KSP 제안 키워드
Characteristics analysis, Cognitive characteristics, Data collected, Driver Behavior, Driver workload, EEG data, Human error, Information systems(IS), Lane change, Large deviations, Left-turn