• Journal of the Korean Association of Geographic Information Studies
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• v.11 no.1
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• pp.90-104
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• 2008

Laser scanning technology with high positional accuracy and high density will be widely applied to vast range of fields including geomatics. Especially, the development of laser scanning technology enabling long range information extraction is increasing its full use in civil engineering. This study taps into the strengths of a terrestrial laser scanning technique to develop a tunnel cross section management system that can be practically employed for determining the cross section of tunnels more promptly and accurately. Three dimensional data with high density were obtained in a prompt and accurate manner using a terrestrial laser scanner. Data processing was then conducted to promptly determine arbitrary cross sections at 0.1meter, 0.5meter and 1.0meter intervals. A laser scanning technique was also used to quickly and accurately calculate the overbreak and underbreak of both each cross section and the entire tunnel section. As the developed system utilizes vast amounts of data, it was possible to promptly determine the shape of arbitrary cross section and to calculate the overbreak and underbreak more accurately with higher area precision. It is expected, therefore, that the system will not only enable more efficient and cost effective tunnel drilling management and monitoring but also will provide a basis for future construction and management of tunnel cross section.

• Journal of the Korea Academia-Industrial cooperation Society
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• v.19 no.2
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• pp.595-601
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• 2018

The era of autonomous vehicles, which drive themselves and in whose operation the driver does not intervene, is fast approaching. The safety of autonomous vehicles can be guaranteed only if they recognize the road infrastructure. However, the road infrastructure consists of road safety facilities, traffic operation systems, and cross-sectional concerns, which include a variety of components, such as types, shapes, and sizes. Therefore, it is necessary to prioritize the road information. This study was conducted to select the priority with which the road infrastructure attributes should be acquired using the AHP (Analytical Hierarchy Process) method. The road infrastructure attributes were categorized into 2 levels, levels 1 and 2, which consisted of 3 and 26 types of attributes, respectively. As a result of the AHP analysis, it was found that the highest priorities of the road infrastructure are the road safety facilities, traffic operation systems and cross sectional concerns. Also, in level-2, the priorities of the safety barriers (road safety facilities), traffic signals (traffic operation systems), and the median (cross sectional) are the highest. Also, this study provides application examples of road infrastructure extraction with the Point Cloud. The results are expected to support the recognition of technology for autonomous vehicles.