• 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.

• The Journal of the Korea institute of electronic communication sciences
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• v.9 no.3
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• pp.329-334
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• 2014

Recently, various electronic functions are developed for car drivers as the advent of electrical automobile. Especially, there are functions to examine for preventing drowsy or healthcare through monitoring brainwave(EEG) of drivers in real time. This function can be provided by transmitting driver's EEG, and the network function for transmission among cars or between car and road side infrastructure is a vital issue. Therefore, in this paper, to provide efficient routing protocol for transmitting EEG data at a cross road in an urban area, 5 different wireless communication network applied each routing protocol such as AODV, DSR, GRP, OLSR, and TORA is designed and simulated in the OPNet network simulator, then it is evaluated for the result.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.39C no.10
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• pp.887-895
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• 2014

Drowsy driving is a large proportion of the total car accidents. 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. Many researches have been published that to measure electroencephalogram(EEG) signals is the effective way in order to be aware of fatigue and drowsiness of drivers. 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, transition, and drowsiness. This paper proposes a drowsiness detection system using errors-in-variables(EIV) for extraction of feature vectors and multilayer perceptron (MLP) for classification. The proposed method evaluates robustness for noise and compares to the previous one using linear predictive coding (LPC) combined with MLP. From evaluation results, we conclude that the proposed scheme outperforms the previous one in the low signal-to-noise ratio regime.

• International Journal of Highway Engineering
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• v.13 no.1
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• pp.149-156
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• 2011

Considerable evaluation is needed to design a new longitudinal tunnel in advance because it damaged drivers' driving safety and heightened the possibility of traffic accidents with its physical characteristics. Specifically, considering traffic psychological and ergonomic factors was very important to prevent the difficulty of maintaining safe speed, the increase of the drowsy driving, the fatality of traffic accidents, and subjective feelings such as anxiety while driving a car through the tunnel, from design to construction. This study dealt with driving safety evaluation of an original road alignment design for the longitudinal tunnel (length: above 10km) with a driving simulator, and helped us to improve an original road alignment design and make an alternative road alignment design with presenting risky districts. The results of experiment showed that inflection points were revealed more risky districts, because they impaired driving safety and elevated driver workload while driving a car through around the inflection points of two-way route. Finally, the limitations and implications of this study were discussed.

• The Journal of the Institute of Internet, Broadcasting and Communication
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• v.20 no.2
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• pp.157-163
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• 2020

Recently, fine dust has been known to cause cardiovascular diseases here, raising interest in ways to reduce emissions by efficiently using fuel from cars that cause air pollution. Accordingly, a driving assistance system was developed to save fuel by eco-driving and improve the driver's bad driving habits. The system was developed using raspberry pi, arduino and Android. Using RPM, speed, fuel injection information obtained from OBD-II, and gyro-sensor values, Fuel-Cut is induced to create an optimal inertial driving environment. It also provides various information system such as weather, driving environment, and preventing drowsy driving through GUI and voice recognition functions. It is possible to check driving records and vehicle fault information using Android application and has low overhead for message transmission using MQTT protocol optimized for IoT environment.

• KSCE Journal of Civil and Environmental Engineering Research
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• v.39 no.3
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• pp.391-398
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• 2019

In general, truck-involved crashes increase severity in terms of both injury level and crash impact level. Recently, although the frequency and fatality of truck-involved crashes in Korea are rising, their associative studies are very limited. Therefore, the objective of this study is to identify critical factors influencing on injury severity of truck-involved crashes on Korean freeway system. To carry out this objective, this study uses an ordered probit model (OPM) based on a 6-year crash dataset from 2012 to 2017. From the analysis, eight variables were found to have a great effect on injury severity: older driver, crash speed, rear-end collision, number of vehicles involved, drowsy driving, nighttime (0:00 to 6:00) driving, overturn or rollover, and vehicle's fire after crash. However, injury severity was less severe in crashes under snowy condition and crashes to traffic facilities (i.e., crash alone).

• Journal of the Institute of Convergence Signal Processing
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• v.17 no.1
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• pp.18-25
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• 2016

As more people have cars, the threat of traffic accidents is posed on men and women of all ages. The main culprit of traffic accidents is driving while intoxicated or drowsy. The method to recognize and prevent the cause of traffic accidents is to use lane detection. In this study, a total of 4,000 frames (day image: 2,900 frames, night image: 1,100 frames) were used to test lane detection. According to the test, in the case of day image, when the threshold of Sobel edge detection technique was detected with second-order differential equation, there was the highest candidate lane detection rate which was 86.1%. In the threshold of Canny edge detection technique, the highest detection rate of 88.0% was found at Low=50, and High=300. In the case of night image, the threshold of Sobel edge detection technique, when horizontal calculation and vertical calculation had second-order differential equation, and when horizontal-vertical calculation had 1.5th-order differential equation, there was the highest detection rate which was 83.1%. In the threshold of Canny edge detection technique, the highest detection rate of 89.9% was found at Low=50, and High=300.