• The Journal of Korean Institute of Communications and Information Sciences
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• v.39A no.6
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• pp.322-332
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• 2014

This paper proposes a robust lane detection algorithm for non-flat roads by combining a piecewise linear model and dynamic programming. Compared with other lane models, the piecewise linear model can represent 3D shapes of roads from the scenes acquired by monocular camera since it can form a curved surface through a set of planar road. To represent the real road, the planar roads are created by various angles and positions at each section. And dynamic programming determines an optimal combination of planar roads based on lane properties. Experiment results demonstrate the robustness of proposed algorithm against non-flat road, curved road, and camera vibration.

• Journal of Korean Society of Transportation
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• v.32 no.6
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• pp.686-693
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• 2014

This study presents a model that enables to predict road surface temperature using neural network theory. Historical road surface temperature data were collected from Road Weather Information System. They used for the calibration of the model. The neural network was designed to predict surface temperature after 1-hour, 2-hour, and 3-hour from now. The developed model was performed on Cheongwon-Sangju highway to test. As a result, the standard deviation of the difference of the predicted and observed was $1.27^{\circ}C$, $0.55^{\circ}C$ and $1.43^{\circ}C$, respectively. Also, comparing the predicted surface temperature and the actual data, R2 was found to be 0.985, 0.923, and 0.903, respectively. It can be concluded that the explanatory power of the model seems to be high.

• Journal of the Institute of Electronics Engineers of Korea SP
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• v.43 no.4 s.310
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• pp.66-73
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• 2006

Safety is the most important for all traffic management and control technology. This paper focuses on developing a flexible, reliable and real-time processing algorithm which is able to generate signal for the entering vehicle at the joining road through a camera and image processing technique. The images obtained from the camera located beside and upon the road can be used for traffic surveillance, the vehicle's travel speed measurement, predicted arriving time in joining area between main road and joining road. And the proposed algorithm displays the confluence safety signal with red, blue and yellow color sign. The three methods are used to detect the vehicle which is driving in setted detecting area. The first method is the gray scale normalized correlation algorithm, and the second is the edge magnitude ratio changing algorithm, and the third is the average intensity changing algorithm The real-time prototype confluence safety signal generation algorithm is implemented on stored digital image sequences of real traffic state and a program with good experimental results.

• Journal of the Korean Institute of Intelligent Systems
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• v.18 no.6
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• pp.797-807
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• 2008

This paper proposes a new algorithm of extracting forward vehicle areas using the acquired lanes and road area information on road images with complex background to improve the efficiency of the vehicle detection. In the first stage, lanes are detected by taking into account the connectivity among the edges which are determined from a method of chain code. Once the lanes proceeding to the same direction with the running vehicle are detected, neighborhood roadways are found from the width and vanishing point of the acquired roadway of the running vehicle. And finally, vehicle areas, where forward vehicles are located on the road area including the center and neighborhood roadways, are extracted. Therefore, the proposed scheme of extracting forward vehicle area improves the rate of vehicle detection on the road images with complex background, and is highly efficient because of detecting vehicles within the confines of the acquired vehicle area. The superiority of the proposed algorithm is verified from experiments of the vehicle detection on road images with complex background.

• Spatial Information Research
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• v.20 no.5
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• pp.57-66
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• 2012

Recent development in wireless communication technology like GPS as well as mobile equipments like PDA and cellular phone makes location-based services (LBSs) popular. However, because users request a query to LBS servers by using their exact locations while moving on the road network, users' privacy may not be protected in the LBSs. Therefore, a mechanism for users' privacy protection is required for the safe and comfortable use of LBSs by mobile users. For this, we, in this paper, propose a road network distance based cloaking scheme supporting user privacy protection in location-based services. The proposed scheme creates a cloaking area by considering road network distance, in order to support the efficient and safe LBSs on the road network. Finally, we show from our performance analysis that our cloaking scheme outperforms the existing cloaking scheme in terms of cloaking area and service time.

• Journal of the Institute of Electronics Engineers of Korea SP
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• v.46 no.5
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• pp.126-134
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• 2009

This paper describes the vision-based system to track road signs from within a moving vehicle. The proposed system has the standard architecture with particle filter due to its robust tracking performance in complex environment. In the case of tracking road signs in real environment, it has a great difficulty in predicting time series data by reason of an occlusion due to an obstacle and the rapid change of objects on roads. To overcome this problem and improve the tracking performance, this paper proposes the algorithm using an autoregressive model as an state transition model which has affine parameters as states and using robust statistics for determining occlusion due to obstacles. The experiments of this paper show that the proposed method is efficient for real time tracking of road signs and performs well in road signs under occlusion due to obstacles.

• The Journal of Korean Institute of Information Technology
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• v.16 no.12
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• pp.69-74
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• 2018

The road name address is a two-dimensional location marking method for naming each road and assigning a number to each building. However, the road name address only shows the necessary parts for administrative and legal acts, and it does not properly display the main characteristics of various roads and non-residential areas. This has become more and more difficult to standardize different location identification methods, merely as a separate location identification method. This paper proposes road-based 3D location identification code to overcome the difficulties of integrating different location identification methods in Korea and to overcome the limit of 2D plane. This is a method to integrate various location identification methods based on roads and to identify spatial coordinates. It is a study on 3D digital coding of the land suitable for the 4th Industrial Revolution era.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.24 no.11
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• pp.1437-1444
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• 2020

With the development of computer vision systems, many advances have been made in the fields of surveillance, biometrics, medical imaging, and autonomous driving. In the field of autonomous driving, in particular, the object detection technique using deep learning are widely used, and the paved road detection is a particularly crucial problem. Unlike the ROI detection algorithm used in general object detection, the structure of paved road in the image is heterogeneous, so the ROI-based object recognition architecture is not available. In this paper, we propose a deep neural network architecture for atypical paved road detection using Semantic segmentation network. In addition, we introduce the multi-scale semantic segmentation network, which is a network architecture specialized to the paved road detection. We demonstrate that the performance is significantly improved by the proposed method.

• Journal of the Society of Disaster Information
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• v.19 no.1
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• pp.184-194
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• 2023

Purpose: The purpose of this study is to identify the characteristics of C02 emissions by road before establishing a policy to reduce greenhouse gas emissions. Method: As for the analysis method, the traffic volume and speed of the road were estimated using the traffic Assignment model targeting 27 arterial road axes in Incheon Metropolitan City. And, after estimating CO2 emissions by road axis by applying this, the characteristics of each group were analyzed through cluster analysis. Result: As a result of cluster analysis using total CO2 emissions, CO2 emissions by truck vehicles, and the ratio of truck vehicle emissions to total carbon dioxide emissions, four clusters were classified. When examining the characteristics of each road included in each group, it was analyzed that the characteristics of each group appeared according to the level of impact by CO2 emissions and truck vehicles. Conclusion: It is judged that it is necessary to establish a plan in consideration of CO2 emission characteristics for road CO2 management for greenhouse gas reduction.

• International conference on construction engineering and project management
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• 2009.05a
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• pp.1183-1188
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• 2009

Estimating project costs during the early stage of conceptual planning is very important when deciding whether to approve the project and allocate an appropriate budget. However, due to greater uncertainties involved in a project, it is challenging to estimate costs during this initial stage within a reasonable tolerance. This paper attempts to develop a cost-estimate model for public road projects under these circumstances and limitations. In the conceptual planning stage of a road project, there is only limited information for cost estimation, for example, such input data as total length of the route, origin and destination, number of lanes, general geographic characteristics of the route, and other basic attributes. This implies that the model should individuate suitable but restricted information without considering detailed features such as quantity of earthwork and a detailed route of a given condition. With these limited facts, this paper applies a case-based reasoning (CBR) method to solve a new problem by deriving similar past problems, which in turn is used to estimate the cost of a given project based on best-fitted previous cases. To develop a CBR cost-estimate model, the authors classified 8 representative variables, including project type, the number of lanes, total length, road design grades, etc. Then, we developed the CBR model, primarily by using 180 actual cases of public road projects, procured over the last decade. With the CBR model, it was found that the degree of error in estimation can be reasonably reduced, to below approximately 30% compared to the final costs estimated upon the completion of detailed design.