• Journal of Biomedical Engineering Research
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• v.18 no.3
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• pp.315-323
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• 1997

Driving is a complex vigilance task that includes improper lookout, excessive speed and inattention. The primary objective of this research is to detect driver drowsiness so that the driver can be alerted to an impending traffic accident in performance. We developed the automatic detection and warning system during drowsy driving. A drowsiness detection system must be able to monitor driver status and detect the detrimental changes of a driver performance. Eyeblink has been found to be a reliable factor of drowsiness detection in earlier studies. As an additional parameter, we also considered the yawning which often occurs in a low vigilance state and predicts the drowsy state. We used a computer vision method to extract the eyeblink and yawning in the face image sequences. When the drowsy state was detected, the driver was refreshed by alarming device and menthol scent generator after deciding the warning level by fuzzy logic. For the evaluation of our system, we measured the physiological parameters such as EOG and EEG. The results indicated that it is possible to detect and alert the driver drowsiness temporarily or continuously by using our system.

• Journal of Korea Society of Digital Industry and Information Management
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• v.5 no.2
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• pp.167-176
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• 2009

In this paper, a system that detects driver's drowsiness has been implemented based on the automatic extraction and the tracking of pupils. The research also focuses on the compensation of illumination and reduction of background noises that naturally exist in the driving condition. The system, that is based on the principle of Haar-like feature, automatically collects data from areas of driver's face and eyes among the complex background. Then, it makes decision of driver's drowsiness by using recognition of characteristics of pupils area, detection of pupils, and their movements. The implemented system has been evaluated and verified the practical uses for the prevention of driver's drowsiness.

• Proceedings of the KOSOMBE Conference
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• v.1996 no.11
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• pp.90-94
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• 1996

We studied the problem of driver's low vigilance state which is related to the one reason of traffic accidents. In this paper, we developed the drowsiness warning system for a drowsy driver. To extract the eyes and mouth from the driver's facial image in real time, a computer vision method was used. The eye blink duration and yawning were used as measurement parameters of drowsiness detection. When the drowsy state of a driver was detected, the driver was refreshed by the scent generator and the alarm. Also, the driver's bio-signal was acquired and analyzed to measure the vigilance state.

• Proceedings of the KOSOMBE Conference
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• v.1998 no.11
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• pp.131-132
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• 1998

The purpose of this study is to keep drivers from falling asleep at the wheel, it is necessary to find ways of detecting and relieving drowsiness. For the estimation of our warning system, we measured the physiological parameters such as EEG, ECG, EOG while they performed a monotonous task intended to induce drowsiness. The effects of a oxygen, odor and various colors on the subjects while in a drowsy state were examined. It was found that a combination of a certain amount of oxygen and odor such as a menthol and yellow color can have a positive effect of relieving drowsiness.

• Proceedings of the KOSOMBE Conference
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• v.1997 no.11
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• pp.48-51
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• 1997

The purpose of this paper is to propose a method to protect the drowsiness of a driver. We measured the physiological signals, response time, and ace expression of the subjects in normal and drowsy state. Those data are used to establish the drowsiness index and fuzzy system. We employed the computer vision technology to extract and eye, track eyelids and measure the parameters related to drowsiness. These parameters were ed into the fuzzy system to decide the drowsiness level, When the drowsiness was detected, the fuzzy system generated warning signals which cons ist of sound and fragrance. Our system was available in decision of the drowsiness level and improvement of subjects' state.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2015.05a
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• pp.947-950
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• 2015

In the cause of car accidents in Korea, drowsy driving has shown that it is larger fctors than drunk driving. Therefore, in order to prevent drowsy driving accidents, drowsiness detection and warning system for drivers has recently become a very important issue. Furthermore, Many researches have been published that measuring alpha wave of EEG signals is the effective way in order to be aware of drowsiness of drivers. In this study, we have developed EEG measuring device that applies a signal processing algorithm using the LabView program for detecting drowsiness. According to results of drowsiness inducement experiments for small test subjects, it was able to detect the pattern of EEG, which means drowsy state based on the changing of power spectrum, counterpart of alpha wave. After all, Comparing to the results of drowsiness pattern between commercial equipments and developed device, we could confirm acquiring similar pattern to drowsiness pattern. With this results, the driver's drowsiness prevention system expect that it will be able to contribute to lowering the death rate caused by drowsy driving accidents.

• Journal of the Institute of Convergence Signal Processing
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• v.13 no.3
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• pp.136-141
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• 2012

One of the main reasons for serious road accidents is driving while drowsy. For this reason, drowsiness detection and warning system for drivers has recently become a very important issue. Monitoring physiological signals provides the possibility of detecting features of drowsiness and fatigue of drivers. One of the effective signals is to measure electroencephalogram (EEG) signals and electrooculogram (EOG) signals. The aim of this study is to extract drowsiness-related features from a set of EEG signals and to classify the features into three states: alertness, drowsiness, sleepiness. This paper proposes a neural-network-based drowsiness detection system using Linear Predictive Coding (LPC) coefficients as feature vectors and Multi-Layer Perceptron (MLP) as a classifier. Samples of EEG data from each predefined state were used to train the MLP program by using the proposed feature extraction algorithms. The trained MLP program was tested on unclassified EEG data and subsequently reviewed according to manual classification. The classification rate of the proposed system is over 96.5% for only very small number of samples (250ms, 64 samples). Therefore, it can be applied to real driving incident situation that can occur for a split second.

• Journal of the Korean Institute of Intelligent Systems
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• v.22 no.6
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• pp.768-773
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• 2012

One of the main reasons for serious road accidents is driving while drowsy. For this reason, drowsiness detection and warning system for drivers has recently become a very important issue. Monitoring physiological signals provides the possibility of detecting features of drowsiness and fatigue of drivers. One of the effective signals is to measure electroencephalogram (EEG) signals and electrooculogram (EOG) signals. The aim of this study is to extract drowsiness-related features from a set of EEG signals and to classify the features into three states: alertness, drowsiness, sleepiness. This paper proposes a drowsiness detection system using Linear Predictive Coding (LPC) coefficients and Support Vector Machine (SVM). Samples of EEG data from each predefined state were used to train the SVM program by using the proposed feature extraction algorithms. The trained SVM program was tested on unclassified EEG data and subsequently reviewed according to manual classification. The classification rate of the proposed system is over 96.5% for only very small number of samples (250ms, 64 samples). Therefore, it can be applied to real driving incident situation that can occur for a split second.

• Journal of Biomedical Engineering Research
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• v.21 no.2
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• pp.175-180
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• 2000

본 논문은 주행 중의 졸음방지를 목적으로 하는 각성시스템의 평가에 관한 연구이다. 졸음운전을 방지하는 데에는 각성도의 저하상태를 높은 정확도로 검출하는 기술과 그것을 해소하는 기술이 필요하다. 본 논문에서는 졸음운저자를 위해서 졸음각성시스템을 향상시켰다. 개발된 각성시스템의 평가를 위해서 졸음을 유도하는 단조로운 행위를 수행하면서 뇌파, 심전도, 안전도와 같은 생체신호를 측정하였다. 피험자가 졸음상태에 있을 때에 산소, 향, 여러 가지 색 자극을 제시함으로써 각성효과를 평가하였다. 졸음의 해소에 효율적인 일정한 양의 산소와 멘톨 성분이 함유된 향을 동시에 각성자극으로 제시하였을 때와, 노란색의 색 자극을 주었을 때 가장 각성에 효과적임을 확인할 수 있었다.

• Journal of Auto-vehicle Safety Association
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• v.8 no.1
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• pp.13-18
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• 2016

More than 80 percent of traffic accidents related with lane departure believed to be the result of crossing the lane due to either negligence or drowsiness of the driver. Lane-departure related accident in the highway usually involve high fatality. Even though LDWS is believed to prevent accident 25% and reduce fatalities by 15% respectively, its effectiveness in performance is yet to be confirmed in many aspects. In this study, the vehicle lateral locations relative to warning zone envelop (earliest and latest warning zone) defined in ISO standard, ECE and NHTSA regulations are compared with respect to various factors including delays, vehicle speed and vehicle heading angle with respect to the lane. Since LDWS is designed to be activated at the speed over 60 km/h, vehicle speed range for the study is set to be from 60 to 100 km/h. The vehicle heading angle (yaw angle) is set to be up to 5 degree away from the lane (abrupt lane change) considering standard for lane change test using double lane-change test specification. The TLC is calculated using factors like vehicle speed, yaw angle and reaction time. In addition, the effect of vehicle type and reaction time have been considered to assess LDWS safety.