• Atmosphere
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• v.24 no.4
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• pp.471-479
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• 2014

A road surface temperature prediction model (UM-Road) using input data of the Unified Model (UM) output and road physical properties is developed and verified with the use of the observed data at road weather information system. The UM outputs of air temperature, relative humidity, wind speed, downward shortwave radiation, net longwave radiation, precipitation and the road properties such as slope angles, albedo, thermal conductivity, heat capacity at maximum 7 depth are used. The net radiation is computed by a surface radiation energy balance, the ground heat flux at surface is estimated by a surface energy balance based on the Monin-Obukhov similarity, the ground heat transfer process is applied to predict the road surface temperature. If the observed road surface temperature exists, the simulated road surface temperature is corrected by mean bias during the last 24 hours. The developed UM-Road is verified using the observed data at road side for the period from 21 to 31 March 2013. It is found that the UM-Road simulates the diurnal trend and peak values of road surface temperature very well and the 50% (90%) of temperature difference lies within ${\pm}1.5^{\circ}C$ (${\pm}2.5^{\circ}C$) except for precipitation case.

• Spatial Information Research
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• v.16 no.1
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• pp.1-10
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• 2008

`Information measure model' and 'Information benefit model' for road plan are developed. In result of application to actual road project, 'Information measure model' showed same result with actual road plan, it can express qualitative element by 'Information' like safety, public discontent, etc. and 'Information benefit model' can evaluate present plan in the side of provider's and demader's 'benefit'.

• Journal of the Korea Academia-Industrial cooperation Society
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• v.15 no.2
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• pp.1195-1203
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• 2014

Since IFC (Industry Foundation Classes) 4 is based on the representation of 3D elements for an architecture project, and does not define standardized shapes for civil projects such as roads, bridges, and tunnels etc, it has limitations in securing interoperability for exchanging a shape information model for the civil projects. Besides, since road facilities have a linear reference, which is modeled along the center alignment, it is difficult the designers to create a standardized 3D road model. The aim of this study is to configure structure elements and their attribute for a road in the perspective of 3D design for developing a shape information model for the road. To solve these issues, this study analyzes the design documents, which consist of a road design handbook, guide, specifications and standards, and then extract shape elements and their attributes of road structures. Such shape elements are defined as an entity item and we review a hierarchical structure of a road shape defined by a virtual road model. The detailed elements and their attributes can be utilized as a 3D shape information model for constructing BIM (Building Information Modeling) environment for Infrastructures. Besides, it is expected that the suggested items will be utilized as a base data for extending to IFC for a road subdividing the detailed shapes, types and attributes for road projects.

• Proceedings of the IEEK Conference
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• 2009.05a
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• pp.222-224
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• 2009

This paper presents an algorithm that extracts road lane's specific information by using HSI color information and performance enhancement of lane detection base on vision processing of drive assist. As a preprocessing for high speed lane detection, the optimal extraction of region of interest for lane boundary(ROI-LB) can be processed to reduction of detection region in which high speed processing is enabled and it also increases reliabilities by deleting edges those are misrecognized. Road lane is extracted with simultaneous processing of noise reduction and edge enhancement using the Laplacian filter, the reliability of feature extraction can be increased for various road lane patterns. Since noise can be removed by using saturation and brightness of HSI color model. Also it searches for the road lane's color information and extracts characteristics. The real road experimental results are presented to evaluate the effectiveness of the proposed method.

• Journal of information and communication convergence engineering
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• v.15 no.3
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• pp.187-192
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• 2017

Autonomous driving vehicle research demands complex road and lane understanding such as lane departure warning, adaptive cruise control, lane keeping and centering, lane change and turn assist, and driving under complex road conditions. A fast and robust road lane detection subsystem is a basic but important building block for this type of research. In this paper, we propose a method that performs road lane detection from black box input. The proposed system applies Random Sample Consensus to find the best model of road lanes passing through divided regions of the input image under HSV color model. HSV color model is chosen since it explicitly separates chromaticity and luminosity and the narrower hue distribution greatly assists in later segmentation of the frames by limiting color saturation. The implemented method was successful in lane detection on real world on-board testing, exhibiting 86.21% accuracy with 4.3% standard deviation in real time.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.17 no.3
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• pp.861-880
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• 2023

Semantic segmentation of road scene is the key technology of autonomous driving, and the improvement of convolutional neural network architecture promotes the improvement of model segmentation performance. The existing convolutional neural network has the simplification of learning knowledge and the complexity of the model. To address this issue, we proposed a road scene semantic segmentation algorithm based on multi-task collaborative learning. Firstly, a depthwise separable convolution atrous spatial pyramid pooling is proposed to reduce model complexity. Secondly, a collaborative learning framework is proposed involved with saliency detection, and the joint loss function is defined using homoscedastic uncertainty to meet the new learning model. Experiments are conducted on the road and nature scenes datasets. The proposed method achieves 70.94% and 64.90% mIoU on Cityscapes and PASCAL VOC 2012 datasets, respectively. Qualitatively, Compared to methods with excellent performance, the method proposed in this paper has significant advantages in the segmentation of fine targets and boundaries.

• Journal of Information Processing Systems
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• v.2 no.1
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• pp.52-57
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• 2006

One of the important functions of an Intelligent Transportation System (ITS) is to classify vehicle types using a vision system. We propose a method using machine-learning algorithms for this classification problem with 3-D object model fitting. It is also necessary to detect road lanes from a fixed traffic surveillance camera in preparation for model fitting. We apply a background mask and line analysis algorithm based on statistical measures to Hough Transform (HT) in order to remove noise and false positive road lanes. The results show that this method is quite efficient in terms of quality.

• Journal of Korean Society of Transportation
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• v.29 no.2
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• pp.71-80
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• 2011

Almost all Drivers who are not familiar with local areas usually rely on road signs equipped along the roadways. The road signs in Korea present the name of the city along the driver’s direction. The consistency of guided name on the road-signs is important for drivers. The discordances among road signs frustrate drivers particularly when the drivers are confused with whether or not they are in the right direction. There are several studies focusing on the continuity of information on the road signs. Most of the researches, however, do not suggest the objective way but diagnose present problem. Applying the Analytic Hierarchy Process (AHP), we evaluate the impact of information on road signs and select the candidate information considering the score and limited number of information. We also suggest the reasonable spatial influence area of road sign information using geo-spatial analysis. From this study, we expect that the director in charge of selecting information can make decision reasonably without difficulties of choosing information.

• Journal of the Korean Society for Library and Information Science
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• v.45 no.1
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• pp.255-285
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• 2011

The purpose of this study is to propose a model for building a road map which could be used for digitalizing library information resources. For this purpose, 941 libraries were surveyed via mail and e-mail. The survey dealt with four strategic areas: production and circulation, construction and management, the sharing and preservation, and application of library resources. It's expected that the model proposed in this study could be applied to develop a plan and road map for digitalizing library information resources.

• Journal of Environmental Impact Assessment
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• v.21 no.4
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• pp.567-572
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• 2012

This paper suggests the set-up plan of the assessment scope in road noise considering road characteristics with the prediction model of road noise. The RLS90 prediction model with some assumptions is used to establish the assessment scope of road noise. The main contents of the applied assumptions are smooth drive of cars, flat region, location of all noise sources in one lane, drive in design speed, and set-up of assessment scope according to traffic volume and car speed. The information of traffic volume to predict road noise is obtained by the distribution of small cars and full-sized cars in road. In this study, the total traffic volume in road is computed by adding the number of small cars to the conversion number of small cars, which means the number of small cars making the same noise as one full-sized car. The prediction result of road noise with the influence factor of traffic volume, car speed, distance between road and receiver is presented. The resultant assessment scope of road noise is obtained by combining road noise prediction data with the set-up standard of road noise assessment scope.