• International Journal of Highway Engineering
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• v.13 no.1
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• pp.177-183
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• 2011

VES-LMC(very-early strength latex modified concrete) has been widely used as repair material for bridge deck overlay or rehabilitation, because it could be opened to the traffic after 3 hours of curing. However, the bright color of VES-LMC disturb driver's sigh. A black VES-LMC, matching to asphalt concrete, was developed and applied at a filed for driver's comfort and safety. The black VES-LMC included 2% carbon black in cement weight ratio. A series of performance evaluation for black VES-LMC was done in terms of field applicability, pavement color and temperature change. The field applicability test result showed that there were no change of workability, slump and air void, and the compressive strengthen developed more than 20MPa after 4 hours of placement. The thermal stress of black VES-LMC was smaller than that of OPC and asphalt concrete, which means the stability of black VES-LMC. The performance evaluation result showed that the black VES-LMC could prevent road icing at below zero temperatures and promote thawing at melting temperature.

• The Journal of the Korea Contents Association
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• v.19 no.12
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• pp.452-459
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• 2019

In order for an autonomous vehicle to be commercialized, it is necessary to conduct a safety test for every aspect. Considering the implementation of the autonomous vehicles technologies to the highest level, it is necessary to analyze the possible scenarios in the most complex environment as in the urban area. It should be confirmed whether autonomous vehicles can be operated with conventional traffic signal environment. It is also required to confirm the ability of autonomous vehicles in interacting with other vehicles, and coping with possible accidents on the road. In this study, the evaluation factors of autonomous vehicles in the road environment are selected by referring to the other evaluation protocols like ADAS. Study result would be reflected in establishing the autonomous vehicle evaluation method for different test environment along with various technology implementation level.

• Journal of the Institute of Electronics Engineers of Korea SC
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• v.49 no.1
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• pp.94-102
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• 2012

Recently entered into an aging socity as the number of elderly drivers is increasing. Traffic accidents of elderly drivers are caused by driver inattentions such as poor vehicle control due to aging, visual information retrieval problems caused by presbyopia, and objects identifying problems caused by low contrast sensitivity. In this paper, detection method of ROIs on the road is proposed. The proposed method creates the saliency map to detect the candidate ROIs from the input image. And, the input image is segmented to obtain the ROIs boundary. Finally, selective visual attention regions are detected according to the presence or absence of a segmented region with saliency pixels. Experimental results from a variety of outdoor environmental conditions, the proposed method presented a fast object detection and a high detection rate.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.19 no.9
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• pp.2114-2120
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• 2015

Vehicle cloud technology is a fusion technology of vehicle communication technology and cloud computing used in wired and wireless Internet, and has attracted attention as a new IT paradigm. It is expected that it would contribute to resolve the road traffic problem with effective communication by providing computer, sensor, communication, device, and resource. but security is necessary to apply vehicle cloud environment and it have to resolve security threats and various attacks occurred in wired and wireless vehicle environment. Therefore, in this paper, we designed security framework to provide secure communication between vehicle and vehicle, and vehicle and the Road side in the vehicle cloud environment. Safety and security of the vehicle environment was satisfied with the security requirements of the vehicle and cloud-based environment, and increased efficiency than the conventional vehicle network communication protocols.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.26 no.12
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• pp.1794-1799
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• 2022

Recently, the increasing number of overloaded vehicles on the road poses a risk to traffic safety, such as falling objects, road damage, and chain collisions due to the abnormal weight distribution, and can cause great damage once an accident occurs. therefore we propose to build an object detection-based AI model to identify overloaded vehicles that cause such social problems. In addition, we present a simple yet effective method to construct an object detection model for the large-scale vehicle images. In particular, we utilize the large-scale of vehicle image sets provided by open AI-Hub, which include the overloaded vehicles. We inspected the specific features of sizes of vehicles and types of image sources, and pre-processed these images to train a deep learning-based object detection model. Also, we propose an integrated system for tracking the detected vehicles. Finally, we demonstrated that the detection performance of the overloaded vehicle was improved by about 23% compared to the one using raw data.

• Journal of Environmental Impact Assessment
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• v.33 no.2
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• pp.74-83
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• 2024

This study aims to estimate roadkill occurrences and investigate influential factors in Chungcheongnam-do, contributing to the establishment of roadkill prevention measures. By comprehensively considering weather, road, and environmental information, machine learning was utilized to estimate roadkill incidents and analyze the importance of each variable, deriving primary influencing factors. The Gradient Boosting Machine (GBM) exhibited the best performance, achieving an accuracy of 92.0%, a recall of 84.6%, an F1-score of 89.2%, and an AUC of 0.907. The key factors affecting roadkill included average local atmospheric pressure (hPa), average ground temperature (℃), month, average dew point temperature (℃), presence of median barriers, and average wind speed (m/s). These findings are anticipated to contribute to roadkill prevention strategies and enhance traffic safety, playing a crucial role in maintaining a balance between ecosystems and road development.

• The Journal of The Korea Institute of Intelligent Transport Systems
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• v.9 no.2
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• pp.23-32
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• 2010

Because ROTA(road traffic authority) analyzes the effects of accident reduction based on the data of 1-year after installation of RLC(red light camera), study of accident reduction effects over year after the installation of RLC is very short. This study deals with the traffic accident reduction during 3 years after the installation of RLC. The objective is to analyze the effects of accident reduction by year using EB method. In pursuing the above, the study uses the 951 accident data occurred at the 20 intersections which RLC are installed. The main results analyzed are as follows. First, the safety performance function (SPF) has been developed by the Poisson regression models which are statistically significant. Second, the results of an Empirical Bayes(EB) analyses showed that the accidents were reduced by the range from 2.73 to 38.75% after 1 year, from 6.85 to 47.36% after 2 year, and from 6.04 to 39.31% after 3 year from the installation of RLC.

• Transactions of the Korean Society of Automotive Engineers
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• v.20 no.1
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• pp.106-111
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• 2012

Recently the usage of bicycle has increased steeply in Korea owing to traffic culture of well- being. In a car to bicycle accident investigation, the throw distance of bicycle is very important factor for reconstructing of the accident. The variables that influence on the throw distance of bicycle can be classified into the factors of vehicle and bicycle. Simulations and collision tests in actual car to bicycle accident were executed for obtaining throw distance of bicycle. The simulations were done by PC-$CRASH^{TM}$ and for actual crash tests sand bags were used for the behavior of bicyclist instead of dummy. Factors considered were vehicle velocity and the moving angles of bicycle, also the types of bicycle and vehicle were fairy cycle and automobile, respectively. From the results, the throw distances of a head-on tire collision of $0^{\circ}$ direction was longer than that of tire crash test of $45^{\circ}$ direction, and the throw distances of a head -on frame crash test of $90^{\circ}$ direction was longer than that of frame crash test of $45^{\circ}$ direction. In addition restitution coefficient between vehicle and bicycle was estimated as about 0.1 with based on actual crash tests. Finally the increaser vehicle velocity the longer the throw distances of bicycle, and the results of simulation were relatively good agreement to the experimental results.

• International Journal of Highway Engineering
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• v.13 no.2
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• pp.49-56
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• 2011

Understanding various casual factors affecting the occurrence of freeway traffic crash is a backbone of deriving effective countermeasures. The first step toward understanding such factors is to identify crash risks on freeways. Unlike existing studies, this study focused on the unsafe vehicle maneuvering that can be detected by in-vehicle sensors. The recent advancement of sensor technologies allows us to gather and analyze detailed microscopic events leading to crash occurrence such as the abrupt change in acceleration. This study used an accelerometer to capture the unsafe events. A set of candidate variables representing unsafe events were derived from analyzing acceleration data obtained by the accelerometer. Then, the crash risk was modeled by the binary logistic regression technique. The probabilistic outcome of crash risk can be provided by the proposed model. An application of the methodology assessing crash risk was presented, and further research items for the successful field implementation were also discussed.

• Journal of Korean Society of Transportation
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• v.28 no.3
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• pp.39-49
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• 2010

Provision of in-vehicle warning information is of keen interest since it can be effectively used to prevent traffic accident on the road. This study evaluates the effectiveness of information provision modalities based on kansei engineering. Various warning information scenarios using different modalities are devised for the evaluation. Statistical data analysis techniques including factor analysis, correlation analysis, and the general linear model are used to assess the user's affect for information modalities. The evaluation result shows that the provision of visual information consisted of 'text and pictogram' leads to higher understandability. The combination of beep sound and voice message' was identified as a more effective modality for auditory warning. In addition, the red color for the blinking warning signal was preferred by users.