• International Journal of Highway Engineering
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• v.15 no.1
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• pp.47-56
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• 2013

PURPOSES: This study is to evaluate a ground water level effect on frost heaving in road pavements. METHODS: The effects of water table on frost heaving in pavement systems were evaluated from the mechanical analysis using FROST program. The input parameters and boundary conditions were determined by considering climates, pavement sections, and material properties specially subgrade soil types in Korea. RESULTS: When the water table located above the freezing depth, amount of frost heaving caused by freezing the water in pavement itself was big enough to damage in pavement system, although pavement system consists of fully non-frost-susceptible materials with sufficient thickness of anti-freezing layer. The amount of frost heaving was decreased rapidly with increasing the distance between the water table and freezing depth. CONCLUSIONS: It was concluded that there is no engineering problems related with frost heaving in practical sense when the distance between freezing depth and water table is over 1.5m for having subgrade soils less than 50% of #200 sieve passing to meet specification on quality control in Korea.

• Transactions of the Korean Society of Mechanical Engineers A
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• v.41 no.2
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• pp.143-147
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• 2017

In this study, a driving load measuring method has developed without utilizing WFT. To measure the driving load, we developed a three-axis load cell with a strain gage. A method to verify the performance of load cells was developed. A system to measure the input load was proposed, and it was verified by evaluation. The measurement error of the impact road surface was found to be less than 20％. However, except under impact road surface conditions, the proposed system can be applied for actual vehicle input load measurement. The influence of tire evaluation tests were carried out through the handling verification evaluation. The input load measurement methods proposed in the present study make performance verification possible without using WFT.

• Journal of the Institute of Electronics and Information Engineers
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• v.51 no.1
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• pp.116-125
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• 2014

Road conditions can provide important information for driving safety in driving assistance systems. The input images usually include unnecessary information and they need to be analyzed only in a region of interest (ROI) to reduce the amount of computation. In this paper, a vision-based road ROI determination algorithm is proposed to detect the road region using the positional information of a vanishing point and line segments. The line segments are detected using Canny's edge detection and Hough transform. The vanishing point is traced by a Kalman filter to reduce the false detection due to noises. The road ROI can be determined automatically and adaptively in every frame after initialization. The proposed method is implemented using C++ and the OpenCV library, and the road ROIs are obtained from various video images of black boxes. The results show that the proposed algorithm is robust.

• Journal of Korean Society of Transportation
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• v.29 no.2
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• pp.123-131
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• 2011

This study proposes a model for road surface temperature prediction on basis of the heat-energy balance equation between atmosphere and road surface. The overall model is consisted of two types of modules: 1) Canopy 1 is used to describe heat transfer between soil surface and atmosphere; and 2) Canopy 2 can reflect the characteristics of pavement type. Input data used in the model run is obtained from the Korea Meteorological For model validation, the observed and predicted surface temperature data are compared using data collected on MoonEui Bridge along CheongWon-Sangju Expressway, and the comparison is made on winter and other seasons separately. Analysis results show that average difference between two temperatures lies within ${\pm}2^{\circ}C$ which is considered as appropriate from a micrometeorology point of view. The model proposed in this paper can be adopted as a useful tool in practical applications for winter maintenance. This study being a fundamental research is anticipated to be a starting point for further development of robust surface road temperature prediction algorithms.

• Journal of the Korean Society of Surveying, Geodesy, Photogrammetry and Cartography
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• v.39 no.6
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• pp.363-369
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• 2021

High-definition road maps are used as the basic infrastructure for autonomous vehicles, so the latest road information must be quickly reflected. However, the current drawing and structural editing process of high-definition road maps are manually performed. In addition, it takes the longest time to generate road lanes, which are the main construction targets. In this study, the point cloud of the road lane markings, in which color types(white, blue, and yellow) were predicted through the PointNet model pre-trained in previous studies, were used as input data. Based on the point cloud, this study proposed a methodology for automatically drawing and structural editing of the layer of road lane markings. To verify the usability of the 3D vector data constructed through the proposed methodology, the accuracy was analyzed according to the quality inspection criteria of high-definition road maps. In the positional accuracy test of the vector data, the RMSE (Root Mean Square Error) for horizontal and vertical errors were within 0.1m to verify suitability. In the structural editing accuracy test of the vector data, the structural editing accuracy of the road lane markings type and kind were 88.235%, respectively, and the usability was verified. Therefore, it was found that the methodology proposed in this study can efficiently construct vector data of road lanes for high-definition road maps.

• International Journal of Highway Engineering
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• v.20 no.2
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• pp.109-118
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• 2018

PURPOSES : This study evaluates the efficiency of snow removal operation resources using data envelopment analysis (DEA). The results of this study can help decision-making strategies, especially for resource allocation for snow removal works on national highways. METHODS : First, regional road management offices (DMUs) for efficiency evaluation were set up, and a database (for years 2012-2016) for analysis was formed. Second, DEA was carried out by selecting input and output variables based on the constructed database. Lastly, based on the results of the DEA, the efficiency of each regional road management office was evaluated. In addition, the potential for future improvement was determined. RESULTS : The results showed that there was a large variation in efficiency of snow removal operation resources by regional offices. CONCLUSIONS : The results of this study imply that the evaluation of efficiency for snow removal operation resources is important when decisions related to snow-removal strategies are made by road management offices.

• Journal of the Korea Academia-Industrial cooperation Society
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• v.12 no.12
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• pp.5856-5864
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• 2011

In this paper, we propose a moving object detection method in road video sequences acquired from a stationary camera. Our proposed method is based on the background subtraction method using Gaussian pyramids in both the background images and input video frames. It is more effective than pixel based subtraction approaches to reduce false detections which come from the mis-registration between current frames and the background image. And to determine a threshold value automatically in subtracted images, we calculate the threshold value using Otsu's method in each frame and then apply a scalar Kalman filtering to the threshold value. Experimental results show that the proposed method effectively detects moving objects in road video images.

• International conference on construction engineering and project management
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• 2017.10a
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• pp.313-320
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• 2017

Price fluctuation contract is imperative and of paramount essence in the construction industry as it provides adequate relief and cushioning for changes in the prices of input resources during construction. As a result, several methods have been devised to better help in arriving at fair recompense in the event of price chang es. However, stakeholders often appear not to be satisfied with the existing methods of fluctuation evaluation, ostensibly because of the challenges associated with them. The aim of this study was to identify the challenges to usage of building construction cost indices in Ghana. Data was gathered from contractors and quantity surveying firms. The study utilized survey questionnaire approach to elicit responses from the contractors and the consultants. Data gathered was analyzed scientifically, using the Relative Importance Index (RII) to rank the problems associated with the existing methods. The findings revealed the following among others; late release of data; inadequate recovery of costs; and work items of interest not included in the published indices as the main challenges of the existing methods. This study will provide useful lessons for policy makers and practitioners in decision making towards the usage and improvement of available indices.

• Journal of Internet Computing and Services
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• v.16 no.2
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• pp.49-55
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• 2015

According to the statistics of traffic accidents over recent 5 years, traffic accidents during the night times happened more than the day times. There are various causes to occur traffic accidents and the one of the major causes is inappropriate or missing street lights that make driver's sight confused and causes the traffic accidents. In this paper, with smartphones, we designed and implemented a lane luminance measurement application which stores the information of driver's location, driving, and lane luminance into database in real time to figure out the inappropriate street light facilities and the area that does not have any street lights. This application is implemented under Native C/C++ environment using android NDK and it improves the operation speed than code written in Java or other languages. To measure the luminance of road, the input image with RGB color space is converted to image with YCbCr color space and Y value returns the luminance of road. The application detects the road lane and calculates the road lane luminance into the database sever. Also this application receives the road video image using smart phone's camera and improves the computational cost by allocating the ROI(Region of interest) of input images. The ROI of image is converted to Grayscale image and then applied the canny edge detector to extract the outline of lanes. After that, we applied hough line transform method to achieve the candidated lane group. The both sides of lane is selected by lane detection algorithm that utilizes the gradient of candidated lanes. When the both lanes of road are detected, we set up a triangle area with a height 20 pixels down from intersection of lanes and the luminance of road is estimated from this triangle area. Y value is calculated from the extracted each R, G, B value of pixels in the triangle. The average Y value of pixels is ranged between from 0 to 100 value to inform a luminance of road and each pixel values are represented with color between black and green. We store car location using smartphone's GPS sensor into the database server after analyzing the road lane video image with luminance of road about 60 meters ahead by wireless communication every 10 minutes. We expect that those collected road luminance information can warn drivers about safe driving or effectively improve the renovation plans of road luminance management.

• The Journal of Korea Institute of Information, Electronics, and Communication Technology
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• v.13 no.5
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• pp.323-330
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• 2020

This paper proposes the road roughness based Braking Pressure Calculation System(BPCS) for an Autonomous Vehicle Stability. The system consists of an image normalization module that processes the front image of a vehicle to fit the input of the random forest, a Random Forest based Road Roughness Classification Module that distinguish the roughness of the road on which the vehicle is travelling by using the weather information and the front image of a vehicle as an input, and a brake pressure control module that modifies a friction coefficient applied to the vehicle according to the road roughness and determines the braking strength to maintain optimal driving according to a vehicle ahead. To verify the efficiency of the BPCS experiment was conducted with a random forest model. The result of the experiment shows that the accuracy of the random forest model was about 2% higher than that of the SVM, and that 7 features should be bagged to make an accurate random forest model. Therefore, the BPCS satisfies both real-time and accuracy in situations where the vehicle needs to brake.