Mapping Grade-Separated Junctions in Detail Using Crowdsourced Trajectory Data

As an important component in transportation maps, three-dimensional (3D) structure information of grade-separated junctions is crucial for applications such as intelligent driving, route planning and traffic control. In order to acquire spatial layouts of road junctions, researchers have developed algorithms to extract planar structures from various data sources. However, it is less common to refine maps of grade-separated junctions with 3D structure information using tracking data. The objective of this study is to find an approach to extracting 3D structures of grade-separated junctions from vehicle trajectories. The proposed method is based on semantic segmentation and data fusion. Trajectories were divided into sections with different trends of elevation by detecting change points. The ranges and elevations of slopes and level sections were derived by seeking consensus among different trajectories using a data fusion technique. Based on semantic segmentation and aggregated elevations, we reconstructed detailed 3D junction structures. This method was validated on multiple crowdsourced trajectory datasets and compared to cluster center linking method. Experiments show that the proposed method had a higher overall accuracy of semantic segmentation than baseline method. The accuracy of vertical relationship at intersections is comparable to baseline. Despite large elevation discrepancy among trajectories, the performance of the proposed method was similar across crowdsourced trajectory datasets from open and commercial projects.

Automated summary

This study focuses on extracting 3D structures of grade-separated junctions from vehicle trajectories using semantic segmentation and data fusion. The proposed method outperformed baseline methods in terms of semantic segmentation accuracy. Despite large elevation discrepancies among trajectories, the performance of the proposed method remained consistent across crowdsourced trajectory datasets.