Recently, Vehicle-to-Everything(V2X) cooperative perception has attracted increasing attention. Infrastructure sensors play a critical role in this research field, however, how to find the optimal placement of infrastructure sensors is rarely studied. In this paper, we investigate the problem of infrastructure sensor placement and propose a pipeline that can efficiently and effectively find optimal installation positions for infrastructure sensors in a realistic simulated environment. To better simulate and evaluate LiDAR placement, we establish a Realistic LiDAR Simulation library that can simulate the unique characteristics of different popular LiDARs and produce high-fidelity LiDAR point clouds in the CARLA simulator. Through simulating point cloud data in different LiDAR placements, we can evaluate the perception accuracy of these placements using multiple detection models. Then, we analyze the correlation between the point cloud distribution and perception accuracy by calculating the density and uniformity of regions of interest. Experiments show that the placement of infrastructure LiDAR can heavily affect the accuracy of perception. We also analyze the correlation between perception performance in the region of interest and LiDAR point cloud distribution and validate that density and uniformity can be indicators of performance.

翻译：近期，车联网（V2X）协同感知技术日益受到关注。基础设施传感器在该研究领域中扮演着关键角色，然而如何确定基础设施传感器的最优布置位置却鲜有研究。本文探讨了基础设施传感器布置问题，提出了一种能够在真实模拟环境中高效、有效地找到基础设施传感器最优安装位置的流水线。为了更逼真地模拟和评估LiDAR布置，我们构建了一个真实LiDAR仿真库，该库能模拟不同主流LiDAR的独特特性，并在CARLA模拟器中生成高保真度的LiDAR点云。通过模拟不同LiDAR布置下的点云数据，我们可利用多种检测模型评估这些布置的感知精度。进而，我们通过计算感兴趣区域的点云密度和均匀性，分析了点云分布与感知精度之间的相关性。实验表明，基础设施LiDAR的布置位置会显著影响感知精度。我们还分析了感兴趣区域内的感知性能与LiDAR点云分布之间的相关性，验证了密度和均匀性可作为性能指标。