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 when using the same number and type of LiDAR, the placement scheme optimized by our proposed method improves the average precision by 15%, compared with the conventional placement scheme in the standard lane scene. 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. Both the RLS Library and related code will be released at https://github.com/PJLab-ADG/LiDARSimLib-and-Placement-Evaluation.

翻译：近年来，车联网协同感知技术日益受到关注。基础设施传感器在此研究领域发挥关键作用，然而如何优化部署基础设施传感器的位置却鲜有研究。本文针对基础设施传感器布局问题展开研究，提出了一套能够在逼真模拟环境中高效寻找基础设施传感器最佳安装位置的流程。为更好模拟和评估激光雷达布局，我们建立了真实激光雷达仿真库，可模拟不同主流激光雷达的独特特性，并在CARLA仿真器中生成高保真激光雷达点云数据。通过模拟不同激光雷达布局下的点云数据，我们可利用多种检测模型评估这些布局的感知精度。进而通过计算感兴趣区域的点云密度与均匀性，分析点云分布与感知精度之间的关联性。实验表明，在相同数量与型号的激光雷达条件下，经本方法优化的布局方案相较于标准车道场景中的传统布局方案，平均精度提升了15%。我们还分析了感兴趣区域的感知性能与激光雷达点云分布之间的关联性，验证了密度与均匀性可作为性能评估指标。RLS库及相关代码将在 https://github.com/PJLab-ADG/LiDARSimLib-and-Placement-Evaluation 发布。