• Journal of the Korea Institute of Information and Communication Engineering
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• v.22 no.3
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• pp.434-442
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• 2018

In this paper, we propose a road lane color recognition method from the image obtained from a driving vehicle. In autonomous vehicle techniques, lane information becomes more important as the level of autonomous driving such as lane departure warning and dynamic lane keeping assistance is increased. In particular the lane color recognition, especially the white and the yellow lanes, is necessary technique because it is directly related to traffic accidents. In this paper, color information of lane and road area is mapped to a 2-dimensional S-color space based on lane detection. And the center of the feature distribution is obtained by using an improved mean-shift algorithm in the S-color space. The lane color is determined by using the distance between the center coordinates of the color features of the left and right lanes and the road area. In various illumination conditions, about 97% color recognition rate is achieved.

• Journal of Korean Society of Transportation
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• v.27 no.2
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• pp.95-105
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• 2009

Traffic accidents on highways are likely to happen when there is an imbalance in the complex relationships among key elements such as road geometries, driver related factors, and mechanical performances. The Demand-Effort Model (DEM), which evaluates highway safety, can be explained by the imbalance, which occurs when the level of demand of the driver's attention to the road environment exceeds that of the response from the driver. This study suggests a new model that improves the reliability of the current DEM through the reinterpretation on the physiological signals with the help of the Neural Network Model (NNM). The data were collected from 149 subjects, who drove a test vehicle on the Yongdong, Honam, and Seohaean Expressways in Korea. Three important results could be drawn from the recursive tests as follows; (1) Only 5 out of 10 parameters on the physiological signals which are currently used were proven to be meaningful through the Normality Test, Cluster Analysis, and Mann-Whitney Analysis. (2) The revised DEM, which internally uses the NNM, showed more reliable results than existing DEM. Group 1, which is based on the new DEM showed 80.0% of accuracy in measuring the level of driver's efforts, however, that of Group 2 based on the current DEM was 74.3%. (3) Field tests on the Honam Expressway showed lower 'type II error' with the new DEM (40.5%) than the old DEM (58.8%). The DEM is designed as a quick and easy way to determine highway safety prior to the minute road safety audit (RSA) by a professional audit team. Then a new DEM, which is based on the NNM, needs to be considered since it showed higher reliability and lower error.

• Analytical Science and Technology
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• v.26 no.4
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• pp.245-255
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• 2013

Identification of paint on victim's clothing and a vehicle are valuable for forensic examination when investigating hit-and-run accidents. Automotive paints on clothes are used to prove a victim caused by traffic accident and to identify a suspected vehicle. The comparison of transferred paints between victim's vehicle and suspected vehicle can be an important evidence in reconstructing the accident situation and in discovering the truth. The paints such as white, yellow, red, blue, or black are hard to examine particle shape under a stereomicroscope because of it is not included aluminum, pearl, and mica flakes in the pigments. The aim of this study under forensic aspect is to compare pigment among basecoat layers of solid paints by identifying inorganic elemental compositions and binder resins of pigments using by micro-FT-IR and micro-XRF spectrometer. The pigment samples were analyzed by using two methods of FT-IR: Reflectance and ATR method. Two methods of FT-IR were useful in discriminating binder resins of pigments by comparing characteristic peaks and patterns of spectra. Also, XRF spectrometer could identify the elemental compositions in inorganic pigments of trace paints which are difficult to compare the identification by FT-IR.

• Journal of the Korean Society of Surveying, Geodesy, Photogrammetry and Cartography
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• v.34 no.3
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• pp.221-230
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• 2016

To establish objective criteria for high pedestrian accident zones, we combined Getis-ord Gi* and Kernel Density Estimation to select high pedestrian accident zones for 54,208 pedestrian accidents in Seoul from 2009 to 2013. By applying Getis-ord Gi* and considering spatial patterns where pedestrian accident hot spots were clustered, this study identified high pedestrian accident zones. The research examined the microscopic distribution of accidents in high pedestrian accident zones, identified the critical hot spots through Kernel Density Estimation, and analyzed the inner distribution of hot spots by identifying the areas with high density levels.

• 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.

• Proceedings of the Korea Information Processing Society Conference
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• 2018.10a
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• pp.656-659
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• 2018

통계적으로 일반인보다 청각장애인의 교통 사고율이 높은 것으로 나타나는데, 이는 청각 장애로 대표되는 차량을 포함한 위험 요소를 인식하기 힘든 상태나 조건에서 기인한다. 자동차가 접근하는 등의 소리를 듣지 못한다는 것은 결국 어떠한 위치에 위험요소가 존재하는지 인지하지 못함에 따라 사고로 이어질 가능성이 존재함을 의미하는데 이러한 문제점을 개선함과 동시에 대화중인 사람의 목소리를 시각화하여 정보를 제공함으로써 청각장애인으로 하여금 더 안전하고 쾌적한 삶을 누리게 하는 것이 청각장애인을 위한 사운드 정보 시각화 안경의 개발 목적이다. 위와 같은 배경을 통해 딥 러닝 기술에 기반하여 분류 과정을 거친 소리 정보의 판별을 통해 위험 요소를 인식한 후 시각화 하여 정보를 제공하는 디바이스를 제안한다.

• Korean Journal of Culture and Social Issue
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• v.17 no.2
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• pp.175-197
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• 2011

This study aimed to expand and reconstruct the Driving Behavior Determinants' factors in order to confirm the relationship between Driving Behavior Determinants(DBD) and drivers' reckless driving behavior level. To expand the structure of DBD, drivers anger, introversion and type A characteristics were added, which were never considered as related factors in existing DBD studies before. The correlations between the new factors of DBD and reckless driving behavior(includes driver's personal records of driving experiences for the last three years) were verified. A factor analysis result showed us that new DBD questionnaire consists of five factors such as, 'Problem Evading', 'Benefits/Sensation Seeking', 'Anti-personal Anxiety', 'Anti-personal Anger', and 'Aggression'. Also, reckless driving behavior consists of 'Speeding', 'Inexperienced Coping', 'Wild Driving', 'Drunken Driving', and 'Distraction'. The result of correlation between the DBD and reckless driving behavior indicates that inappropriate level of DBD is highly correlated with dangerous driving behavior and strong possibilities of traffic accidents. Based on these results, we might be able to discriminate drivers according to DBD level and predict their reckless driving behavior through a standardization procedure. Futhermore, this will make us to provide drivers differentiated safety education service.

• The Journal of The Korea Institute of Intelligent Transport Systems
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• v.16 no.6
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• pp.141-155
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• 2017

As the use of SNS becomes more active, many people are posting their thoughts about specific events in their SNS in the form of text. As a result, SNS is used in various fields such as finance and distribution to conduct service satisfaction surveys and consumer monitoring. However, in the transportation area, there are not enough cases to utilize unstructured data analysis such as emotional analysis. In this study, we developed an emotional analysis methodology that can be used in transportation by using highway VOC data, which is atypical data collected by Korea Expressway Corporation. The developed methodology consists of morpheme analysis, emotional dictionary construction, and emotional discrimination of the collected unstructured data. The developed methodology was verified using highway related tweet data. As a result of the analysis, it can be guessed that many information and information about the construction and the accident were related to the highway during the analysis period. Also, it seems that users complain about the delay caused by construction and accident.

• International Journal of Highway Engineering
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• v.16 no.6
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• pp.149-158
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• 2014

PURPOSES : The bridge section of the expressway has a worse driving environment than the general section. However, traffic safety countermeasures are focused only on the bridge section. Traffic safety countermeasures on the section before entry to the bridge and the section after exit from the bridge are applied only when the bridge has a long-span section. Accordingly, this study will verify the necessity of extending the application of traffic safety countermeasures to areas that are affected by the bridge. METHODS : This study determines the areas that are affected by the bridge as well as the areas that are affected by locations with frequent traffic accidents and suggests the risk factors by affected areas through canonical discriminant analysis. For the analysis, traffic accident data for 3 years, which occurred on bridge sections in six major expressway lines, were used. RESULTS : The numbers of traffic accidents were 469 before the bridge, 281 on the bridge, and 468 after the bridge. The variables that have impact on the seriousness of accidents are as follows: speeding, excess manipulation of the steering wheel, and failure to secure safety distance for accidents that occurred before the bridge section; speeding, excess manipulation of the steering wheel, and dozing off for accidents that occurred on the bridge; and speeding and failure to secure safety distance for accidents that occurred after the bridge section. CONCLUSIONS : Areas affected by the bridge show higher accident rates than the bridge section; therefore, imposing traffic safety countermeasures on the integrated section of the bridge and the affected areas is required. It is believed that the results suggested in this study could be effectively used in the prevention of traffic accidents by imposing custom-made safety countermeasures for each section.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.26 no.6
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• pp.859-864
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• 2022

Drowsy driving, one of the biggest causes of traffic accidents every year, is accompanied by various factors. As a general method to check whether or not there is drowsiness, a method of identifying a driver's expression and driving pattern, and a method of analyzing bio-signals are being studied. This paper proposes a driver fatigue detection system using deep learning technology and bio-signal measurement technology. As the first step in the proposed method, deep learning is used to detect the driver's eye shape, yawning presence, and body movement to detect drowsiness. In the second stage, it was designed to increase the accuracy of the system by identifying the driver's fatigue state using the pulse wave signal and body temperature. As a result of the experiment, it was possible to reliably determine the driver's drowsiness and fatigue in real-time images.