[논문]고속도로 커브구간에서 운전자의 운전부하와 감마파 특성분석에 관한 연구

강학건; 남궁문; 김원철; 왕위걸

doi:10.7470/jkst.2016.34.6.560

고속도로 커브구간에서 운전자의 운전부하와 감마파 특성분석에 관한 연구
The Analysis of Driving Workload and Gamma Waves on Curved Sections in Expressway 원문보기

大韓交通學會誌 = Journal of Korean Society of Transportation, v.34 no.6, 2016년, pp.560 - 569

강학건 (원광대학교 토목환경공학과) , 남궁문 (원광대학교 토목환경공학과) , 김원철 (충남연구원 지역도시연구부) , 왕위걸 (남경대학교 교통과학공학과)

초록
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운전자의 정신부하는 교통사고를 감소하는데 중요한 역할을 하는 것으로 선행연구에서 나타나고 있다. 본 연구에서는 도로 및 환경요소 뿐만 아니라 운전자의 알파파, 베타파, 감마파를 측정할 수 있는 운전시뮬레이터를 활용하여 분석자료를 확보하였다. 운전자의 운전부하와 감마파의 연관성을 분석하기 위한 방법으로 로지스틱모형을 적용하였다. 분석결과, 도로의 커브가 많을수록 운전자의 베타 영역은 증가하는 반면 알파와 감마 영역은 감소되는 것으로 나타났다. 그리고, 운전부하는 감마영역과 음의 상관관계를 지닌 것으로 나타났다. 결론적으로, 직선구간에서의 도로주행이 운전자의 스트레스를 줄이고 행복감을 높일 수 있을 것으로 판단된다.

Abstract ▼ AI-Helper

Previous studies show that driver mental workload plays a significant role in the occurrence of traffic accidents. This study also analyzes driving workload under different road conditions especially with the brain wave data collected by a driving simulator. We use a logistic regression model to explain the relationship between driving workload and gamma brain waves. The results show that beta band of brain waves becomes broader with more curved sections while alpha band and gamma band become narrower. Since driving workload is negatively correlated with gamma band, it can be concluded that driving condition with less curved section is beneficial for reducing driving stress and increasing driving comfort.

주제어

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제안 방법

By regression equation, the driver frontal lobe, temporal lobe, parietal lobe and occipital lobe four kinds workload and the gamma (γ) band were analyzed.
By simulating driving, drivers’ workload under different road conditions (straight lines, single curves, S-shaped curves, and winding curves) was analyzed.
While the study succeeded in the analysis of mental workload, the study design limits the diversity of the data. Firstly, the study designed a fixed traffic flow, and vehicles traveling on a fixed vehicle line as the baseline conditions. Secondly, the conditions of road operation are single, and the complexity of traffic environment is not fully considered.
For the four functional areas of the brain, the beta (β), alpha (α), gamma (γ) waves and the relationship between workload and gamma (γ) waves under different road conditions were analyzed.
In this experiment, the driver driving activity passed through the winding curve, straight road, “S” shaped curve, straight road and single curve.
In this study, we analyzed the EEG of drivers driving along bending roads through a simulation test to understand the characteristics of the drivers’ EEG for workloads under different road conditions, such as for a straight road, an S-shaped curve, and a winding curve.
Accordingly, the visual and sound system, cabin and CFLS, simulation software, and electrical motion platform were included. The software configuration comprised a SCANeR studio, which provided actual driving environments (i.e., vehicles, traffic conditions, and road alignments) for analysis. The simulator was good at evaluating and validating the road-driver-vehicle interactions.

대상 데이터

The experiment was based on the route from Singal JC (61.2 km) to Incheon IC (79.6 km) of the Yeongdong Expressway in Gyeonggi-do, Korea. A total distance of 20 km had to be driven in the driving simulation experiment.
The simulated driving test system for the actual information on the highway was installed in a computer by using three-dimensional (3D) computer graphics with a 3D view module according to the topography of the road, construction, and other vegetation environments. The test drivers wore brainwave testers. Face-LAB was used to obtain the driving maneuvering data.

이론/모형

In this work, we used the relaxation / stress index (β - α) / α method to determine the extent of the workload of the driver in the driving process (Oh et al., 2015; Klimesch, 1999; Scerbo et al., 2003; Smith et al., 2001).

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저자의 다른 논문 :

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용어 설명: PCR은 세균 특이성이 있는 primer를 이용하여 적은 수의 세균이 있을지라도 쉽게 검출할 수 있는 유용한 방법이며, 이를 이용하여 구강 내 치면세균막이나 타액에서 직접 세균을 검출할 수 있게 되었다[8].

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