• Journal of Convergence for Information Technology
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• v.8 no.3
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• pp.193-198
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• 2018

Recently, the casualties of automobile traffic accidents are rapidly increasing, and serious accidents involving serious injury and death are increasing more than those of ordinary people. More than 70% of major accidents occur in drowsy driving. Therefore, in this paper, we studied the drowsiness prevention system to prevent large-scale disasters of traffic accidents. In this paper, we propose a real-time flicker recognition method for drowsy driving detection system and drowsy recognition according to the increase of carbon dioxide. The drowsy driving detection system applied the existing image detection and the deep running, and the carbon dioxide detection was developed based on the IoT. The drowsy prevention system using both of these techniques improved the accuracy compared to the existing products.

• Journal of Biomedical Engineering Research
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• v.19 no.6
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• pp.577-584
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• 1998

• Proceedings of the Safety Management and Science Conference
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• 2003.05a
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• pp.143-147
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• 2003

This study is to develop a device of bath-seat with massage which is attached to bath-seat and is able to sustain massage on perineal region and anus effectively. And also develop new programmed driving-seat that is able to protect drowsiness. This seat is operated easily by user controlling time period (eg. 30 minutes or 60 minutes) of vibration. During the vibration, user can make a choice of making n sound which is proved as a better effects for concentration and comfort.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.14 no.5
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• pp.1303-1310
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• 2010

Recently, researches related with automative mechanism have been widely studied to increase the driver's safety by continuously monitoring the driver's health condition to prevent driver's drowsiness. This paper describes the design of wearable chest belt for ECG and reflectance pulse oximetry for SpO2 sensors based on wireless sensor network to monitor the driver's healthcare status. ECG, SpO2 and heart rate signals can be transmitted via wireless sensor node to base station connected to the server. Intelligent monitoring system is designed at the server to analyze the SpO2 and ECG signals. HRV (Heart Rate Variability) signals can be obtained by processing the ECG and PPG signals. HRV signals are further analyzed based on time and frequency domain to determine the driver's drowsiness status.

• 제어로봇시스템학회:학술대회논문집
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• 1997.10a
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• pp.1856-1859
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• 1997

This paper describes the arousal measurement and the control system using fuzzy logic to prevent drowsy driving. Sugeno's method was used for fuzzy inference in this study. Membership function and rule base were determined form the modfied arousal level criteria. The output of fuzzy inference tracked well the change of subject's arousal level. When IRI(Inter-SIR interval) was under the 60sec, maximum output of three step warning method was medium sound, but that of fuzzy logic system was changed from medium to big. Furthermore, the output of the fuzzy inference was highly correlated with $N_{z}$(r=0.99). Therefore, the fuzzy inference method for evaluation and the control of arousal will be more effective at real driving sityation than three step warning method.ning method.

• Transactions of the Korean Society of Automotive Engineers
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• v.21 no.5
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• pp.54-58
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• 2013

In recent days, vehicles have become equipped with electric systems that assist and help drivers driving safe by reducing possible accidents. LDWS(Lane Departure Warning System) and LKAS(Lane Keeping Assistant System) are involved in assist systems, especially for lateral motion of vehicles. Sudden and inattentive lateral motion of vehicles due to drivers' fatigue, illness, inattention, and drowsiness are major causes of accidents in highway. LDWS and LKAS provide drivers with warnings or assisting power to reduce any possibilities of accidents. In order to prevent or minimize the possibilities of accidents, lateral motion control of vehicles has been introduced in this research. DGPS/RTK(Differential Global Positioning System/Real Time Kinematics) and GIS(Geographic Information System) have been used to obtain the current position of vehicles and decide when activate controlling lateral motion of vehicles. The presented lateral motion control has been validated with actual vehicle tests.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.12 no.8
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• pp.3820-3841
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• 2018

Most of the accidents occur due to drowsiness while driving, avoiding road signs and due to driver's distraction. Driver's distraction depends on various factors which include talking with passengers while driving, mood disorder, nervousness, anger, over-excitement, anxiety, loud music, illness, fatigue and different driver's head rotations due to change in yaw, pitch and roll angle. The contribution of this paper is two-fold. Firstly, a data set is generated for conducting different experiments on driver's distraction. Secondly, novel approaches are presented that use features based on facial points; especially the features computed using motion vectors and interpolation to detect a special type of driver's distraction, i.e., driver's head rotation due to change in yaw angle. These facial points are detected by Active Shape Model (ASM) and Boosted Regression with Markov Networks (BoRMaN). Various types of classifiers are trained and tested on different frames to decide about a driver's distraction. These approaches are also scale invariant. The results show that the approach that uses the novel ideas of motion vectors and interpolation outperforms other approaches in detection of driver's head rotation. We are able to achieve a percentage accuracy of 98.45 using Neural Network.

• International Journal of Highway Engineering
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• v.18 no.3
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• pp.127-134
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• 2016

PURPOSES : Low visibility caused by dark surroundings at nighttime affects the likelihood of accidents, and various efforts, such as installing road safety facilities, have been made to reduce accidents at night. Despite these efforts, the nighttime severity index (SI) in Korea was higher than the daytime SI during 2011-2014. This study determined the factors affecting daytime and nighttime accident severity through a discriminant analysis. METHODS : Discriminant analysis. RESULTS : First, drowsiness, lack of attention, and lighting facilities affected both daytime and nighttime accident severity. Accidents were found to be caused by a low ability to recognize the driving conditions and a low obstacle avoidance capability. Second, road conditions and speeding affected only the daytime accident severity. Third, failure to maintain a safe distance significantly affected daytime accident severity and nonsignificantly affected nighttime accident severity. The majority of such accidents were caused by rear-end collisions of vehicles driving in the same direction; given the low relative speed difference in such cases, the shock imparted by the accidents was minimal. CONCLUSIONS : Accidents caused by a failure to maintain a safe distance has lower severity than do accidents caused by other factors.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.11 no.8
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• pp.3965-3983
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• 2017

We propose a novel eye tracking method that can estimate 3D world coordinates using an infrared (IR) stereo camera for indoor and outdoor environments. This method first detects dark evidences such as eyes, eyebrows and mouths by fast multi-level thresholding. Among these evidences, eye pair evidences are detected by evidential reasoning and geometrical rules. For robust accuracy, two classifiers based on multiple layer perceptron (MLP) using gradient local binary patterns (GLBPs) verify whether the detected evidences are real eye pairs or not. Finally, the 3D world coordinates of detected eyes are calculated by region-based stereo matching. Compared with other eye detection methods, the proposed method can detect the eyes of people wearing sunglasses due to the use of the IR spectrum. Especially, when people are in dark environments such as driving at nighttime, driving in an indoor carpark, or passing through a tunnel, human eyes can be robustly detected because we use active IR illuminators. In the experimental results, it is shown that the proposed method can detect eye pairs with high performance in real-time under variable illumination conditions. Therefore, the proposed method can contribute to human-computer interactions (HCIs) and intelligent transportation systems (ITSs) applications such as gaze tracking, windshield head-up display and drowsiness detection.

• The Journal of the Convergence on Culture Technology
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• v.8 no.6
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• pp.905-910
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• 2022

Drowsy driving requires a lot of social attention because it increases the incidence of traffic accidents and leads to fatal accidents. The number of accidents caused by drowsy driving is increasing every year. Therefore, in order to solve this problem all over the world, research for measuring various biosignals is being conducted. Among them, this paper focuses on non-contact biosignal analysis. Various noises such as engine, tire, and body vibrations are generated in a running vehicle. To measure the driver's heart rate and respiration rate in a driving vehicle with a piezoelectric sensor, a sensor plate that can cushion vehicle vibrations was designed and noise generated from the vehicle was reduced. In addition, we developed a system for classifying whether the driver is sleeping or not by extracting the model using the CNN-LSTM ensemble learning technique based on the signal of the piezoelectric sensor. In order to learn the sleep state, the subject's biosignals were acquired every 30 seconds, and 797 pieces of data were comparatively analyzed.