An Efficient Background Filtering Method for Roadside LiDARs

In the context of connected and automated transportation systems (CATSs), light detection and ranging (LiDAR) is increasingly being deployed on the roadside to detect detailed motions of road users (i.e., vehicles and pedestrians) for real-time applications. To provide real-time detection, it is essential to conduct background filtering of the LiDAR point cloud to eliminate LiDAR points irrelevant to the traffic objects. Background filtering can significantly reduce the computational load of implementing a traffic-detection algorithm. However, existing methods are not sufficiently fast and accurate for real-time applications. This study proposes an efficient method of background filtering method for roadside LiDAR data by solving the problems of existing methods in the background filtering procedure. In the proposed method, the octree is used to aggregate LiDAR frames, which can dramatically reduce storage space compared to simply superposing frames in existing studies. Second, by integrating ray casting and occupancy ratio (RCOR), the background can be extracted according to the spatial relations and statistical probabilities of objects. In the final stage, a sparse voxel octree (SVO) is applied to represent the background, and a GPU-based parallel filtering algorithm can expedite background filtering significantly. We conducted a field experiment to collect LiDAR data using various LiDARs installed at the roadside of a freeway segment in Chengdu, China. The results demonstrate that the proposed method performs best in terms of accuracy and computation speed in a comparison experiment. Its performance can remain robust with various types of LiDARs under various traffic conditions.