Version 1
: Received: 6 December 2021 / Approved: 7 December 2021 / Online: 7 December 2021 (23:44:09 CET)
How to cite:
Nguyen, H.; Jeong, H. An Area Partitioning Approach to the Conflation of Road Networks with Highly Different Level of Details. Preprints2021, 2021120112. https://doi.org/10.20944/preprints202112.0112.v1
Nguyen, H.; Jeong, H. An Area Partitioning Approach to the Conflation of Road Networks with Highly Different Level of Details. Preprints 2021, 2021120112. https://doi.org/10.20944/preprints202112.0112.v1
Nguyen, H.; Jeong, H. An Area Partitioning Approach to the Conflation of Road Networks with Highly Different Level of Details. Preprints2021, 2021120112. https://doi.org/10.20944/preprints202112.0112.v1
APA Style
Nguyen, H., & Jeong, H. (2021). An Area Partitioning Approach to the Conflation of Road Networks with Highly Different Level of Details. Preprints. https://doi.org/10.20944/preprints202112.0112.v1
Chicago/Turabian Style
Nguyen, H. and Han-You Jeong. 2021 "An Area Partitioning Approach to the Conflation of Road Networks with Highly Different Level of Details" Preprints. https://doi.org/10.20944/preprints202112.0112.v1
Abstract
A road network represents road objects in a given geographic area and their interconnections, and is an essential component of intelligent transportation systems (ITS) enabling emerging new applications such as dynamic route guidance, driving assistance systems, and autonomous driving. As the digitization of geospatial information becomes prevalent, a number of road networks with a wide variety of characteristics coexist. In this paper, we present an area partitioning approach to the conflation of two road networks with a large difference in level of details. Our approach first partitions the geographic area by the Network Voronoi Area Diagram (NVAD) of low-detailed road network. Next, a subgraph of high-detailed road network corresponding to a complex intersection is extracted and then aggregated into a supernode so that a high matching precision can be achieved via 1:1 node matching. To improve the matching recall, we also present a few schemes that address the problem of missing corresponding object and representation dissimilarity between these road networks. Numerical results at Yeouido, Korea's autonomous vehicle testing site, show that our area partitioning approach can significantly improve the performance of road network matching.
Computer Science and Mathematics, Computer Science
Copyright:
This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
