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학술지 A Validation Study on a Subjective Driving Workload Prediction Tool
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저자
황윤숙, 윤대섭, 김현숙, 김경호
발행일
201408
출처
IEEE Transactions on Intelligent Transportation Systems, v.15 no.4, pp.1835-1843
ISSN
1524-9050
출판사
IEEE
DOI
https://dx.doi.org/10.1109/TITS.2014.2334664
협약과제
13VC4200, 운전부하 정량화 및 지능형 인터페이스 관리 기술 개발, 윤대섭
초록
A variety of methods used to measure a driver's workload do not include information such as the driver's characteristics and attitudes. A subjective driving workload prediction tool (DWPT) was developed to overcome this limitation. The purpose of this study is to validate the DWPT, which is composed of three subfactors: the situational inadaptability, the risk-taking personality, and the interpersonal inadaptability. For this reason, we conducted the driving simulator experiment to gather the drivers' driving behaviors. The driving path scenario included various driving tasks. Thirty male drivers participated in this study. The analysis results showed that a driver's predicted score of subjective driving workload had a positive or a negative relation to their workload-related driving behaviors such as the operation of the indicator/steering/gas pedal and gaze behaviors. In particular, two subfactors, i.e., the risk-taking personality and the interpersonal inadaptability, were more closely related to their driving behaviors than the total predicted subjective driving workload and the situational inadaptability subfactor. These results suggest that a DWPT could be used to predict the drivers' subjective driving workload instead of measuring the driving performance or self-reporting questionnaire. In addition, this would be expected to be available on the area of the Advanced Driver Assistance System and drivers' safety industry. © 2014 IEEE.
키워드
Automotive application, human factors, human-computer interaction, interactive systems, road accidents
KSP 제안 키워드
Advanced driver assistance systems(ADAS), Automotive Applications, Driving Performance, Driving simulator experiment, Human Factors, Interactive system, Prediction tool, S characteristics, Validation study, Workload prediction, driving behavior