A Novel Information Theory-Based Metric for Evaluating Roadside LiDAR Placement

Abstract

Intelligent vehicle infrastructure cooperative systems can essentially address the bottleneck of the limited perception range of autonomous vehicles. This is achieved by the real-time perception of road traffic using roadside mounted sensors, which is receiving increasing attention. Light detection and ranging (LiDAR) has progressively been deployed on the roadside due to its advantage of remaining unaffected by lights and the capability to obtain 3-D information about traffic participants. However, a practical evaluation criterion is currently lacking for assessing the merits of roadside LiDAR placement. In this study, we propose an information-theoretic LiDAR mounting placement evaluation method, which simplifies the complex problem of LiDAR mounting placement to a simple parameter calculation problem. We characterize the amount of information detected by LiDAR in the region of interest (ROI) by introducing the per voxel point clouds’ metric. The resulting metric, named the maximum density gain, quantifies the effect of LiDAR mounting placements on perception performance. A bidirectional eight-lane traffic scenario is constructed in Unity3D, and datasets of simulated LiDARs are collected under two traffic conditions: with and without obstruction. The Pointpillars is used to test the collected dataset, which is a point cloud-based 3-D object detection algorithm. The results of Pointpillars and our proposed method are compared to test the validity of the proposed method. The experimental results show that our proposed method is compatible with the outcomes of Pointpillars while being simpler and computationally efficient. The proposed LiDAR mounting placement evaluation method can offer a reliable calculation reference for the mounting placement of roadside LiDARs.