A Joint Optimization of Beam Distribution and Deployment for Roadside LiDAR Systems to Maximize Vehicle Perception

The perception system based on roadside LiDAR is an important component for autonomous driving. However, previous studies optimizing the deployment configuration of roadside LiDAR systems were constrained by LiDAR designs that failed to provide uniform coverage of vehicle point clouds within the perception area. Our objective is to enhance the perception capability of roadside sensing systems by considering both the inherent properties of LiDAR (e.g., beam distribution) and its relationship to roadside infrastructure, particularly in terms of placement height. Firstly, we proposed an analytical model to establish a quantitative relationship between LiDAR configurations and the distribution of vehicle point clouds. We conducted a field test to generate real vehicle point clouds and validate the proposed analytical model using three types of LiDARs. Secondly, we optimized the roadside LiDAR configurations to maximize the perception entropy of vehicle point clouds. We designed a particle swarm optimization algorithm using initial configurations generated through the bisection method as prior knowledge. The use of a particle swarm optimization algorithm narrowed down the search area, thereby expediting the optimization process. A set of simulation-based experiments was conducted using the NGSIM I-80 dataset. Compared to three alternative configurations, the proposed joint optimization model showed the most significant improvement for the 16-beam and 32-beam LiDAR models, with detection
Recall
increasing by 65% and 63%, respectively. These improvements are 4.8 times and 3 times higher than those of the base configuration. This study provides an effective method for LiDAR manufacturers and traffic professionals to optimize roadside LiDAR systems, thereby enhancing vehicle perception capabilities.