지원사업
학술연구/단체지원/교육 등 연구자 활동을 지속하도록 DBpia가 지원하고 있어요.
커뮤니티
연구자들이 자신의 연구와 전문성을 널리 알리고, 새로운 협력의 기회를 만들 수 있는 네트워킹 공간이에요.
논문 기본 정보
- 자료유형
- 학술대회자료
- 저자정보
- 발행연도
- 2013.5
- 수록면
- 31 - 34 (4page)
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초록· 키워드
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Objective: The purpose of this study is to investigate the relationship of driving workload between the DWPT(the subjective Driving-Workload Prediction Tool) developed by ETRI and EEG data collected from real driving environments on curve negotiation in the local road. Background: There are mainly three methods to measure the drivers’ driving workload; the subjective measures using questionnaire, driving performance measures using vehicle information, and drivers’ physiological measures using physiological sensors. However, it is not easy to extract drivers’ characteristics and drivers’ driving attitudes even though if we use above three methods to measure drivers’ driving workload. To overcome these limitations, We had developed the DWPT based on these drivers’ characteristics and attitudes as the part of HVI(Human-Vehicle Interface) project. Method: The total of 27 drivers(male 15, female 12) participated in this study. This experiment was conducted using the FOT(Field Operational Test) method that participants are asked to drive pre-defined path on the real local road. EEG data were collected from the participant while driving. Also, participants are asked to answer the DWPT questionnaire. We had analyzed the collected data in two driving scenarios; curve negotiation and straight road. Results: As the result of correlation analysis, DWPT was not correlated with EEG signal on the straight road. However, the relations between DWPT and EEG signal had positive correlation significantly on curve negotiation. In sub-factor analysis, inadaptability of road circumstances was correlated with EEG data. Conclusion: These results suggested that DWPT developed by ETRI had determined and predicted drivers’ real driving workload on curve negotiation. Application: The DWPT is going to be applied to driving Workload Management System and driver adaptive intelligent Human-Vehicle Interface system as a sub-module in the future intelligent car.
목차
ABSTRACT
1. Introduction
2. Method
3. Results
4. Conclusion
References
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