Next Article in Journal
Multi-Scale Remote Sensing Semantic Analysis Based on a Global Perspective
Previous Article in Journal
Exploring Trusted Relations among Virtual Interactions in Social Networks for Detecting Influence Diffusion
Previous Article in Special Issue
Satellite-Based Bathymetric Modeling Using a Wavelet Network Model
Open AccessArticle

Generation of Lane-Level Road Networks Based on a Trajectory-Similarity-Join Pruning Strategy

by Ling Zheng 1,2, Huashan Song 3, Bijun Li 1,2,* and Hongjuan Zhang 1,2
1
State Key Laboratory of Information Engineering in Surveying, Mapping, and Remote Sensing, Wuhan University, Wuhan 430079, China
2
Engineering Research Center for Spatio-Temporal Data Smart Acquisition and Application, Ministry of Education of China, Wuhan 430079, China
3
Three Gorges Geotechnical Consultants Co., Ltd., Wuhan 430074, China
*
Author to whom correspondence should be addressed.
ISPRS Int. J. Geo-Inf. 2019, 8(9), 416; https://doi.org/10.3390/ijgi8090416
Received: 3 June 2019 / Revised: 4 September 2019 / Accepted: 8 September 2019 / Published: 16 September 2019
With the development of autonomous driving, lane-level maps have attracted significant attention. Since the lane-level road network is an important part of the lane-level map, the efficient, low-cost, and automatic generation of lane-level road networks has become increasingly important. We propose a new method here that generates lane-level road networks using only position information based on an autonomous vehicle and the existing lane-level road networks from the existing road-level professionally surveyed without lane details. This method uses the parallel relationship between the centerline of a lane and the centerline of the corresponding segment. Since the direct point-by-point computation is huge, we propose a method based on a trajectory-similarity-join pruning strategy (TSJ-PS). This method uses a filter-and-verify search framework. First, it performs quick segmentation based on the minimum distance and then uses the similarity of two trajectories to prune the trajectory similarity join. Next, it calculates the centerline trajectory for lanes using the simulation transformation model by the unpruned trajectory points. Finally, we demonstrate the efficiency of the algorithm and generate a lane-level road network via experiments on a real road. View Full-Text
Keywords: intelligent driving; lane-level road network; trajectory-similarity-join pruning strategy (TSJ-PS) intelligent driving; lane-level road network; trajectory-similarity-join pruning strategy (TSJ-PS)
Show Figures

Figure 1

MDPI and ACS Style

Zheng, L.; Song, H.; Li, B.; Zhang, H. Generation of Lane-Level Road Networks Based on a Trajectory-Similarity-Join Pruning Strategy. ISPRS Int. J. Geo-Inf. 2019, 8, 416.

Show more citation formats Show less citations formats
Note that from the first issue of 2016, MDPI journals use article numbers instead of page numbers. See further details here.

Article Access Map by Country/Region

1
Back to TopTop