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Open AccessArticle

A High-Definition Road-Network Model for Self-Driving Vehicles

1
State Key Laboratory of Information Engineering in Surveying, Mapping and Remote Sensing, Wuhan University, Wuhan 430079, China
2
School of Data and Computer Science, Sun Yat-Sen University, Guangzhou 510006, China
*
Author to whom correspondence should be addressed.
ISPRS Int. J. Geo-Inf. 2018, 7(11), 417; https://doi.org/10.3390/ijgi7110417
Received: 4 September 2018 / Revised: 12 October 2018 / Accepted: 26 October 2018 / Published: 29 October 2018
High-definition (HD) maps have gained increasing attention in highly automated driving technology and show great significance for self-driving cars. An HD road network (HDRN) is one of the most important parts of an HD map. To date, there have been few studies focusing on road and road-segment extraction in the automatic generation of an HDRN. To improve the precision of an HDRN further and represent the topological relations between road segments and lanes better, in this paper, we propose an HDRN model (HDRNM) for a self-driving car. The HDRNM divides the HDRN into a road-segment network layer and a road-network layer. It includes road segments, attributes and geometric topological relations between lanes, as well as relations between road segments and lanes. We define the place in a road segment where the attribute changes as a linear event point. The road segment serves as a linear benchmark, and the linear event point from the road segment is mapped to its lanes via their relative positions to segment the lanes. Then, the HDRN is automatically generated from road centerlines collected by a mobile mapping vehicle through a multi-directional constraint principal component analysis method. Finally, an experiment proves the effectiveness of this HDRNM. View Full-Text
Keywords: road network; HDRNM; linear benchmark; PCA road network; HDRNM; linear benchmark; PCA
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Zheng, L.; Li, B.; Zhang, H.; Shan, Y.; Zhou, J. A High-Definition Road-Network Model for Self-Driving Vehicles. ISPRS Int. J. Geo-Inf. 2018, 7, 417.

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