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http://dx.doi.org/10.7848/ksgpc.2021.39.6.363

Automatic Drawing and Structural Editing of Road Lane Markings for High-Definition Road Maps  

Choi, In Ha (Department of Spatial Information Engineering, Namseoul University)
Kim, Eui Myoung (Department of Drone.GIS Engineering, Namseoul University)
Publication Information
Journal of the Korean Society of Surveying, Geodesy, Photogrammetry and Cartography / v.39, no.6, 2021 , pp. 363-369 More about this Journal
Abstract
High-definition road maps are used as the basic infrastructure for autonomous vehicles, so the latest road information must be quickly reflected. However, the current drawing and structural editing process of high-definition road maps are manually performed. In addition, it takes the longest time to generate road lanes, which are the main construction targets. In this study, the point cloud of the road lane markings, in which color types(white, blue, and yellow) were predicted through the PointNet model pre-trained in previous studies, were used as input data. Based on the point cloud, this study proposed a methodology for automatically drawing and structural editing of the layer of road lane markings. To verify the usability of the 3D vector data constructed through the proposed methodology, the accuracy was analyzed according to the quality inspection criteria of high-definition road maps. In the positional accuracy test of the vector data, the RMSE (Root Mean Square Error) for horizontal and vertical errors were within 0.1m to verify suitability. In the structural editing accuracy test of the vector data, the structural editing accuracy of the road lane markings type and kind were 88.235%, respectively, and the usability was verified. Therefore, it was found that the methodology proposed in this study can efficiently construct vector data of road lanes for high-definition road maps.
Keywords
High-Definition Road Maps; Point Cloud; Road Lane Markings; Drawing; Structural Editing; Quality Inspection;
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1 Qi, C.R, Su, H., Mo, K., and Gulibas, L.J (2017), PointNet: deep learning on point sets for 3D classification and segmentation, Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pp. 652-660.
2 National Geographic Information Institute (2020), Manual of quality inspection criteria of the high-definition road maps, pp. 1-136.
3 National Geographic Information Institute (2021), Explanation and guidance materials of high-definition road maps, pp. 1-51.
4 Choi, H.K. (2021), A study and suggestion of digital twin realization in korean new deal, Review of Architecture and Building Science, Vol. 65, No. 3, pp. 38-42.
5 Choi, I.H. and Kim, E.M. (2021), Automatic construction of deep learning training data for high-definition road maps using mobile mapping system, The Journal of Korea Society of Surveying, Geodesy, Photogrammetry, and Cartography, Vol. 39, No. 3, pp. 133-139. (in Korean with English abstract)   DOI
6 Choi, K.J. and Kim, K.H. (2021), An automatic extraction method for road maps using LiDAR-based 3D point maps, The Korean Institute of Information Scientists and Engineers, Vol. 27, No. 5, pp. 234-240. (in Korean with English abstract)
7 Choi, T.S., Yoon, H.S., Choi, Y.S., Lee, W.J., and Chang, S.Y (2020), A study on high definition road map construction using aerial photography, The Journal of Korean Society for Geospatial Information Science, Vol. 28, No. 3, pp. 69-76. (in Korean with English abstract)
8 Korean Society of Transportation (2021), Trend of Policy, Transportation Technology and Policy, Vol. 18, No. 2, pp. 90-92.