Abstract
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.
Highlights
The development of intelligent transportation and advanced driver assistance system (ADAS) has attracted significant attention in academia and industry [1,2,3]
We focus on an HD road-network model (HDRNM)
According to our experimental results, high precision and better representation of the topological relations between road segments and lanes of the HDRNM were achieved in the road-network data derived from mobile measuring vehicles
Summary
The development of intelligent transportation and advanced driver assistance system (ADAS) has attracted significant attention in academia and industry [1,2,3]. Based on prior knowledge of maps and dynamic transportation information, HD maps help self-driving vehicles determine the best driving path and a reasonable driving strategy using global path planning [10,11,12], effectively enhancing driving safety and reducing driving complexity [13]. The creation of HD maps is important, and they are currently in high demand [14]. Road-network data represent roads in the real world, and HD road-network data are an important component of HD maps. We focus on an HD road-network model (HDRNM). To obtain an HDRNM for self-driving vehicles, road-network data must first be collected. Modeling must be performed based on the road-segment network and lane network, as well as on the relations between them. The HDRNM can be applied in different ways to meet different self-driving requirements
Sign-in/Register to view highlights & summary Sign-in/Register to access full text options 
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a callDisclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.
