NDLI: Image-Based Geo-Specific Road Database Creation for Driving Simulation

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Image-Based Geo-Specific Road Database Creation for Driving Simulation

Content Provider	Semantic Scholar
Author	Guo, Dahai Weeks, Arthur R. Klee, Harold I.
Copyright Year	2005
Abstract	Geo-specific road databases are sometimes necessary for driving simulation studies. However, manually creating them is very time consuming. One of the reasons is that a real road can have a non-uniform cross sections due to irregular shaped road boundaries. Additionally, in the University of Central Florida (UCF) driving simulator system, the road database format requires identification of repeatable cross sections, which is also a labor intensive process. While some commercial software applications, such as the MultiGen Road Tool have road modeling functions roads with non-uniform cross-sectional profiles are still hard to model. In this research, an image understanding based method is proposed for geo-specific road database development. Digital Line Graph (DLG) data, issued by the United States Geographical Survey (USGS) is used to limit the search space for road segmentation. The mean-shift clustering method is chosen to be the solution for separating pavement and non-pavement areas within road areas. The Linde-Buzo-Gray (LBG) vector quantization method is used to identify repeatable cross sections. The proposed method results in an average accuracy of road segmentation above 85%. The average error of cross-sectional profile is no more than 0.36 feet. The utilization of the two types of geographical information, high-resolution aerial photos and USGS DLG data, play the most important role in the proposed system.
File Format	PDF HTM / HTML
Alternate Webpage(s)	http://www.nads-sc.uiowa.edu/dscna/2005/papers/ImageBased_GeoSpecific_Road_Database_Creation.pdf
Language	English
Access Restriction	Open
Content Type	Text
Resource Type	Article

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