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김광섭(Kwangseub Kim),Wang Maosen,정낙탁(Naktak Jung),양성모(Seongmo Yang),유세훈(Sehoon Yoo),기대성(Daeseong Gi),서명원(Myungwon Suh) 한국자동차공학회 2015 한국자동차공학회 부문종합 학술대회 Vol.2015 No.5
Drowsy behavior is more likely to occur in sleep-deprived drivers. The accident rate is higher driver of drowsiness. Individuals’ drowsy behavior detection technology should be developed to prevent drowsiness related crashes. Driving information such as accelerations, steering angles and velocity, and physiological signals of drivers such as electroencephalogram (EEG), and eye tracking were adopted in present drowsy behavior detection technologies. However, it is difficult to measure physiological signal, as a result, driving information becomes more popular for drowsy driving detection. In this paper, vehicle information including lateral accelerations, longitudinal acceleration and steering angles, was combined into various cases to detect drowsy driving behavior. In order to increase the accuracy, it is defined the data set and predicted drowsy driving prediction using the Random Forest algorithm. As the result, the case of lateral and longitudinal of acceleration showed the best.