Augmented Multiple Vehicles' Trajectories Extraction Under Occlusions With Roadside LiDAR Data

Object occlusion is a common issue in Light Detection and Ranging (LiDAR)-based vehicle tracking technology. The occlusions can cause variance in vehicle location and speed calculation. How to link the vehicle trajectories caused by occlusion issues is a challenge for traffic engineers and researchers. This paper developed an augmented vehicle tracking method under occlusions with the roadside LiDAR data. The proposed method can be divided into two parts. The first part based on the corner point is used to choose a representative vehicle tracking point. And the second part based on the GNN algorithm is employed to link the vehicles' trajectories under two occlusion situations. The performance of the proposed method has been evaluated using roadside lidar data collected from four different scenarios. The test results showed that more than 89% of disconnected trajectories can be fixed with the proposed method, which is superior compared to the state-of-the-art method. The proposed method can benefit a lot of transportation areas, such as traffic volume count, vehicle speed tracking, and traffic safety analysis.

Automated summary

This paper presents an augmented vehicle tracking method utilizing roadside LiDAR data to address occlusion-induced issues in linking vehicle trajectories. Through a two-part approach, over 89% of disconnected trajectories were successfully fixed, demonstrating superior performance compared to the state-of-the-art methods.