• Journal of the Korean Association of Geographic Information Studies
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• v.21 no.1
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• pp.1-11
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• 2018

Mapping accurate spatial information is important for constructing three-dimensional (3D) spatial models and managing artificial facilities, and, especially, mapping road centerlines is necessary for constructing accurate road maps. This research developed a semi-automatic methodology for mapping road centerlines using the MMS(Mobile Mapping System) LiDAR(Light Detection And Ranging) point cloud as follows. First, the intensity image was generated from the given MMS LiDAR data through the interpolation method. Next, the line segments were extracted from the intensity image through the edge detection technique. Finally, the road centerline segments were manually selected among the extracted line segments. The statistical results showed that the generated road centerlines had 0.065 m overall accuracy but had some errors in the areas near road signs.

• Proceedings of the KSRS Conference
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• 2002.10a
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• pp.34-39
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• 2002

Road information is very important for topographic mapping, transportation application, urban planning and other related application fields. Therefore, automatic detection of road networks from spatial imagery, such as aerial photos and satellite imagery can play a central role in road information acquisition. In this paper, we use least squares correlation matching alone for road center tracking and show that it works. We assumed that (bright) road centerlines would be visible in the image. We further assumed that within a same road segment, there would be only small differences in brightness values. This algorithm works by defining a template around a user-given input point, which shall lie on a road centerline, and then by matching the template against the image along the orientation of the road under consideration. Once matching succeeds, new match proceeds by shifting a matched target window further along road orientation at the target window. By repeating the process above, we obtain a series of points, which lie on a road centerline successively. A 1m resolution IKONOS images over Seoul and Daejeon were used for tests. The results showed that this algorithm could extract road centerlines in any orientation and help in fast and exact he ad-up digitization/vectorization of cartographic images.

• International Journal of Precision Engineering and Manufacturing
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• v.6 no.4
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• pp.3-7
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• 2005

Recently, vehicle simulators are widely used to evaluate driver s responses and driver assistance systems. It needs much effort to construct the virtual driving environment for a vehicle simulator. In this study, it is described how to make effectively the roads and the driving environment for a vehicle simulator. GIS (Geographic Information System) is used to construct the roads and the environment effectively. Because the GIS is the integrated system of geographical data, it contains useful data to make virtual driving environment. First, boundaries and centerlines of roads are extracted from the GIS. From boundaries, the road width is calculated. Using centerlines, mesh models of roads are constructed. The final graphic model of roads is constructed by mapping road images to those mesh models considering the number of lanes and the kind of surface. Data of buildings from the GIS are extracted. Each shape and height of building is determined considering the kind of building to construct the final graphic model of buildings. Then, the graphic model of roadside trees is constructed to decide their locations. Finally, the driving environment for driving simulator is constructed by converting the three graphic models with the graphic format of Direct-X and by joining the three graphic models.

• Proceedings of the KSRS Conference
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• 2003.11a
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• pp.183-185
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• 2003

Spatially referenced mobile mapping (MM) images contain rich information of man-made objects , e.g. road centerlines, buildings, light poles, traffic signs ,billboards and line trees etc. Therefore, the applications in transportation, urban 3D reconstruction, utility management are implemented increasingly. It’s a fundamental issue lies in MM image process that how to orient this image in the object space including interior orientation of camera and the exterior orientation of image. In this paper, the algorithm of automatic acquirement of DC (Digital Camera) parameters based on MM images is illustrated. And then, the mapping between image space and object space for MM images is described.

• International Journal of Highway Engineering
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• v.14 no.3
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• pp.131-140
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• 2012

Lane markings such as edgelines, centerlines, and lines that delineate lanes generally provide drivers with the various information for safe driving. Drivers can easily recognize the lane markings through the color differences between the markings and road surfaces during the daytime. However, it is a bit difficult for drivers to perceive them during the nighttime due to the lack of artificial lights. Although the glass beads with the 1.5-refractive index have been used to improve the visibility of the lane markings during the nighttime, it is still difficult for drivers to recognize the lane markings properly, especially during the rainy nighttime, which may often lead to traffic accidents. To improve the retroreflectivity and visibility of the lane markings during the rainy nighttime, the high refractive beads with the 2.4-refractive index are essentially required, but they do not work appropriately during the dry nighttime. Thus, the mixed materials with the 1.5, 1.9, and 2.4-refractive beads should be considered for the satisfactory implementation of the lane markings. This study reveals the best mixing rates of the beads by conducting benefit-cost analysis under various weather conditions in Korea. The analysis results show that the lane markings with the 100% of the 2.4-refractive beads provide the highest visibility of lane markings regardless of the roadway conditions, but the benefit-cost (B/C) ratio of the bead mixture is merely 0.46. The best mixing rate of the beads, from the highest B/C ratio viewpoint, was identified as the mixture with a 80% of 1.5-refractive beads and a 20% of 2.4-refractive beads. Some limitations and future research agenda have also been discussed.