PURPOSES : In this study, the installation of drowsy rest areas and accidents are analyzed. The factors that affected the accidents caused by drowsy drivers in rest areas are analyzed to improve the safety of rest areas. METHODS : By comparing and analyzing the installation status of the rest areas for drowsy drivers, the accident status were analyzed. The logistic regression model was used to analyze the factors that affect accidents in the drowsy rest area. RESULTS : Most rest areas were installed below the installation criteria. Several accidents occurred when the vehicle entered the drowsy rest area. These rest areas had a short entry ramp, and no safety facilities were installed. The logistic regression model showed that the risk of an accident is lowered when the deceleration lane is longer than 215 m. Additionally, the risk of an accident is lowered when the rest area is installed in the straight section or the curve section, wherein the curve radius is greater than 2 km. CONCLUSIONS : In this study, we evaluated the installation status of the rest areas for drowsy drivers by comparing installation elements. Most rest areas for drowsy drivers were installed at different lengths of the ramp. Some of these were installed on the slope or curved sections of the road. We analyzed the accident status and developed an accident modal using the logistic regression model to identify the factors that affect accidents. It will be necessary to analyze accidents in drowsy rest areas continuously to improve safety for drowsy drivers.

PURPOSES: This study analyzed the differences in drivers’behaviors and car movements between drowsy driving conditions and normal driving. METHODS: This study analyzed behavior data from 32 participants and the related car movement data in a field test under drowsy driving and normal driving conditions. Acquired data were closed-eye time, distance between the test car and the left lane, distance between the test car and the right lane, driving speed, video clip of the driver’s face during driving, and so on. A total of 30 samples for drowsy driving and normal driving were selected once errors had been excluded. RESULTS: There were three factors that differed between drowsy driving and normal driving conditions: closed-eye time, distance between the test car and the left lane, and distance between the test car and the right lane. These results were significant at the 0.05 level. CONCLUSIONS : This study shows that there are three factors that vary significantly between drowsy driving and normal driving conditions that can be useful for detecting drowsy driving. Future studies should be designed with these results in mind, further considering, age, type of road, study site, and so on.

PURPOSES: This study was initiated to estimate the benefits from the campaign to prevent drowsy driving crashes on expressways. The study was conducted by the Korea Expressway Corporation using a contingent valuation method. METHODS : First, a questionnaire was designed for a preliminary survey. From the survey’s results, the initial willingness to pay for the campaign was determined by averaging different amounts of payments chosen under virtual scenarios in the survey. The willingness to pay data was used to find a first bid price for the open-ended method used for the second survey. After that, a primary questionnaire was designed and conducted using a single dichotomous choice question (SDBCQ). Drivers at expressway resting areas were asked their willingness to pay for the campaign. Based on statistical analysis using data collected from the second survey, the mean willingness to pay was estimated using a probability utility function. Finally, the benefit from the campaign was calculated using the estimated willingness to pay and accident data on expressways. CONCLUSIONS : Based on the result from the contingent valuation method, the benefit from the campaign to prevent drowsy driving crashes was estimated to be 170.6 won per expressway trip. The benefit is to be paid as an additional toll. In addition, the traffic crash cost estimate is about 2,209,680,000 won less than the cost during the same period in 2014.

본 논문에서는 컬러정보와 깊이정보를 사용하여 얼굴을 검출하고 추적한 후 항해사의 졸음을 탐지하는 방법을 제안한다. 이 방법은 얼굴검출 과정과 얼굴추적 과정으로 구성된다. 얼굴검출 과정에서는 기존의 방법 중 가장 좋은 성능을 보이는 Adaboost 알고리즘을 사용하며, Adaboost로 입력되는 영상의 영역을 사람이 존재하는 영역으로만 제한하여 얼굴을 검출한다. 얼굴검출 과정에서 얼굴이 검출되면 그것을 템플릿으로 하여 얼굴추적 과정이 수행된다. 제안한 방법의 성능을 평가하기 위하여 실험영상을 이용하여 실험을 수행하였다. 실험결과 제안한 졸음탐지 방법은 기존의 방법에 비해 약 23 %의 수행시간을 보였으며, 또한 졸음탐지 방법은 추적시간과 추적 정확도에 있어서 상보적인 관계를 가지며, 특별한 경우를 제외한 모든 경우에서 약 1 %의 낮은 추적오차율을 보였다.