The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences
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Articles | Volume XLIII-B1-2022
Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci., XLIII-B1-2022, 263–270, 2022
https://doi.org/10.5194/isprs-archives-XLIII-B1-2022-263-2022
Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci., XLIII-B1-2022, 263–270, 2022
https://doi.org/10.5194/isprs-archives-XLIII-B1-2022-263-2022
 
30 May 2022
30 May 2022

AUTOMATED MODELLING OF ROAD FOR HIGH-DEFINITION MAPS WITH OPENDRIVE FORMAT UTILIZING MOBILE MAPPING MEASUREMENTS

H.-Y. Pai, J.-C. Zeng, M.-L. Tsai, and K.-W. Cheng H.-Y. Pai et al.
  • NCKU, Department of Geomatics, No. 1, Daxue Rd., East Dist., Tainan City 701401, Taiwan

Keywords: OpenDRIVE, Extraction of Road Factors, Lane Lines, Point Cloud, Automated Modelling, HD Maps

Abstract. High-definition maps (HD Maps) becomes a trend supporting autonomous vehicles (AVs) which provides accurate auxiliary information about geometries of road, such as center lines of lanes, geometries of roads, traffic signs, etc. It is not restricted by the severe environment, lack of Global Navigation Satellite System (GNSS) signal, or torrential rain, for Avs; furthermore, the standard of HD Maps is defined as tens of centimeters positioning level. However, the production cost of HD Maps is enormous involving human resources and time. The general way of producing HD Maps is to vectorize the roads from point clouds to shapefiles and import them into Geographic Information System (GIS) software to generate HD Maps. In order to simplify this complicated process, this article purposes an algorithm to automated generate high-definition maps from road marks’ data which is extracted from point clouds. The methodology in this article is mainly dived into three steps, extraction of road marks, classify lane lines, and modelling OpenDRIVE files. The achieved 2D and 3D accuracies of proposed algorithm in first fields are about 0.069 m in 2D and 0.107 m, respectively.