• Transactions of the Korean Society of Automotive Engineers
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• v.19 no.5
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• pp.82-91
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• 2011

In this paper, a planar model for mechanics of a vehicle/pedestrian collision incorporating road gradient is derived to evaluate the pre-collision speed of vehicle. It takes into account a few physical variables and parameters of popular wrap and forward projection collisions, which include horizontal distance traveled between primary and secondary impacts with the vehicle, launch angle, center-of-gravity height at launch, distance from launch to rest, pedestrian-ground drag factor, the pre-collision vehicle speed and road gradient. The model including road gradient is derived analytically for reconstruction of pedestrian collision accidents, and evaluates the vehicle speed from the pedestrian throw distance. The model coefficients have physical interpretations and are determined through direct calculation. This work shows that the road gradient has a significant effect on the evaluation of the vehicle speed and must be considered in accident cases with inclined road. In additions, foreign/domestic empirical cases and multibody dynamic simulation results are used to construct a least-squares fitted model that has the same structure of the analytical one that provides an estimate of the vehicle speed based on the pedestrian throw distance and the band within which the vehicle speed would be expected to be in 95% of cases.

• Journal of the Korean Society of Systems Engineering
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• v.1 no.2
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• pp.26-33
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• 2005

In this paper, an algorithm is presented to recognize road based on unpaved test courses image. The road images obtained by a video camera undergoes a pre-processing that includes filtering, gray level slicing, masking and identification of unpaved test courses. After this pre-processing, a part of image is grouped into 27 sub-windows and fed into a three-layer feed-forward neural network. The neural network is trained to indicate the road direction. The proposed algorithm has been tested with the images different from the training images, and demonstrated its efficacy for recognizing unpaved road. Based on the test results, it can be said that the algorithm successfully combines the traditional image processing and the neural network principles towards a simpler and more efficient driver warning or assistance system.

• Journal of the Korean Association of Geographic Information Studies
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• v.10 no.2
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• pp.152-163
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• 2007

The on-going pre-environmental investigation at present is performed by separate numerical analysis of each provision which makes integrated pre-environmental investigation is difficult. The application of numerical data is insufficient, which results to the deterioration of environmental investigation result's objectivity. A lot of time and money is required for the investigation. In this study, the spacial analysis function of GIS was applied on the 8 pre-environmental investigation factors. Pre-environmental investigation GIS DMS(Decision Making System) using AHP was constructed to make integrated investigation possible through the use of investigation results for each factor. Through the use of the developed pre-environmental investigation GIS DMS and the pre-constructed GIS data, the objectivity of environmental investigation is sufficient and time and cost are reduced. Therefore, this system can be used for pre-environmental investigation during route selection in the initial stages of road construction. Through the numerical and visual data obtained from the system developed in this paper, it is easier to gain the approval of the public. Furthermore, environmental problems due to road construction can be investigated with less time and money during the initial stages of road construction.

• Journal of IKEEE
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• v.11 no.4
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• pp.348-357
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• 2007

Due to the improved performance and cost of personal mobile devices and rapid progress of wireless communication technology, the number of users who utilize these devices is increasing. This trend requires various types of services be available to users. So far, there have been many solutions provided for the shortest path problem. But, technologies which can offer various recommendation services to user depending on user’s current location are focused on Euclidean spaces rather than road network. Thus, in this paper, we extend the previous work to satisfy this requirement on road network database. Our proposed scheme requires pre-computation for the efficient query processing. In the preprocessing step, we first partition the input road network into a fixed number of Voronoi polygons and then pre-compute routing information for each polygon. In the meantime, we select the number of Voronoi polygons in proposition to the scale of road network. Through this selection, the required size of pre-computation is linearly increasing to the size of road network. Using this pre-computated information, we can process queries more quickly. Through experiments, we have shown that our proposed scheme can achieve excellent performance in terms of scheduling time and the number of visited nodes.

• Journal of the Korean Society for Precision Engineering
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• v.16 no.10
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• pp.25-34
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• 1999

In this paper, an algorithm is presented to recognize lane and obstacles based on highway road image. The road images obtained by a video camera undergoes a pre-processing that includes filtering, edge detection, and identification of lanes. After this pre-processing, a part of image is grouped into 27 sub-windows and fed into a three-layer feed-forward neural network. The neural network is trained to indicate the road direction and the presence of absence of an obstacle. The proposed algorithm has been tested with the images different from the training images, and demonstrated its efficacy for recognizing lane and obstacles. Based on the test results, it can be said that the algorithm successfully combines the traditional image processing and the neural network principles towards a simpler and more efficient driver warning of assistance system

• Journal of Environmental Science International
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• v.14 no.10
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• pp.905-910
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• 2005

The purpose of this study is the comparative analysis of Susan citizens' images of Eulsook-do as a coastal tourism destination before and after the construction of a road bridge across the Nakdong estuary in order to analyze local people's changes in leisure patterns. Analysis of the images of a pre-construction Eulsook-do that people aged both 40 and less and 50 and more had on five dimensions showed values higher than zero(0) that suggests neutral image, while their images of a post-construction Eulsook-do showed the shrinking size of pentagon on all five dimensions: ET(Entertainment), CA(Culture & Art), EE(Environment & Ecology), RC(Recreation) and LP(Leports) dimensions. Its pre- and post- construction image analysis conducted 20 years after it came to be built finds that the road bridge construction has led to the ecological, environmental disruption of the coast and the lower Nakdong river, having negative influence on the images of Eulsook-so.

• Journal of KIISE:Databases
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• v.36 no.4
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• pp.314-320
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• 2009

Skyline queries have recently received considerable attention in the searching services. The skyline contains interesting objects that are not dominated by any other objects on all dimensions. Many related works have processed a skyline on static data or on moving objects in Euclidean space. However, this paper assumes that the point of a skyline query continuously moves in road networks. We propose a new method that efficiently processes continuous skyline queries in road networks through pre-computed shortest range data of objects. Our experiments show that the proposed method is about 100 times faster than previous methods in terms of query processing time.

• Journal of Positioning, Navigation, and Timing
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• v.9 no.1
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• pp.23-31
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• 2020

It is essential to estimate the vehicle localization for an autonomous safety driving. In particular, since LIDAR provides precise scan data, many studies carried out to estimate the vehicle localization using LIDAR and pre-generated map. The road marking always exists on the road because of provides driving information. Therefore, it is often used for map information. In this paper, we propose to generate the Gaussian mixture map based on road-marking information and localization method using this map. Generally, the probability distributions map stores the single Gaussian distribution for each grid. However, single resolution probability distributions map cannot express complex shapes when grid resolution is large. In addition, when grid resolution is small, map size is bigger and process time is longer. Therefore, it is difficult to apply the road marking. On the other hand, Gaussian mixture distribution can effectively express the road marking by several probability distributions. In this paper, we generate Gaussian mixture map and perform vehicle localization using Gaussian mixture map. Localization performance is analyzed through the experimental result.

• Proceedings of the KSRS Conference
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• 2007.10a
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• pp.234-237
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• 2007

Road networks are important geospatial databases for various GIS (Geographic Information System) applications. Road digital maps may contain geometric spatial errors due to human and scanning errors, but manually updating roads information is time consuming. In this paper, we developed a new road features updating methodology using from multispectral high-resolution satellite image and pre-existing vector map. The approach is based on initial seed point generation using line segment matching and a modified double snake algorithm. Firstly, we conducted line segment matching between the road vector data and the edges of image obtained by Canny operator. Then, the translated road data was used to initialize the seed points of the double snake model in order to refine the updating of road features. The double snake algorithm is composed of two open snake models which are evolving jointly to keep a parallel between them. In the proposed algorithm, a new energy term was added which behaved as a constraint. It forced the snake nodes not to be out of potential road pixels in multispectral image. The experiment was accomplished using a QuickBird pan-sharpened multispectral image and 1:5,000 digital road maps of Daejeon. We showed the feasibility of the approach by presenting results in this urban area.

• KSCE Journal of Civil and Environmental Engineering Research
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• v.26 no.1D
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• pp.185-193
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• 2006

The on-going pre-environmental investigation at present is performed by separate numerical analysis of each provision which makes integrated pre-environmental investigation is difficult. The application of numerical data is insufficient, which results to the deterioration of environmental investigation result's objectivity. A lot of time and money is required for the investigation. In this study, the spacial analysis function of GIS was applied on the 8 pre-environmental investigation factors. Pre-environmental investigation GIS DMS(Decision Making System) was constructed to make integrated investigation possible through the use of investigation results for each factor. Through the use of the developed pre-environmental investigation GIS DMS and the pre-constructed GIS data, the objectivity of environmental investigation is sufficient and time and cost are reduced. Therefore, this system can be used for pre-environmental investigation during route selection in the initial stages of road construction. Through the numerical and visual data obtained from the system developed in this paper, it is easier to gain the approval of the public. Furthermore, environmental problems due to road construction can be investigated with less time and money during the initial stages of road construction.