Infrastructure-based collective perception, which entails the real-time sharing and merging of sensing data from different roadside sensors for object detection, has shown promise in preventing occlusions for traffic safety and efficiency. However, its adoption has been hindered by the lack of guidance for roadside sensor placement and high costs for ex-post evaluation. For infrastructure projects with limited budgets, the ex-ante evaluation for optimizing the configurations and placements of infrastructure sensors is crucial to minimize occlusion risks at a low cost. This paper presents algorithms and simulation tools to support the ex-ante evaluation of the cost-performance tradeoff in infrastructure sensor deployment for collective perception. More specifically, the deployment of infrastructure sensors is framed as an integer programming problem that can be efficiently solved in polynomial time, achieving near-optimal results with the use of certain heuristic algorithms. The solutions provide guidance on deciding sensor locations, installation heights, and configurations to achieve the balance between procurement cost, physical constraints for installation, and sensing coverage. Additionally, we implement the proposed algorithms in a simulation engine. This allows us to evaluate the effectiveness of each sensor deployment solution through the lens of object detection. The application of the proposed methods was illustrated through a case study on traffic monitoring by using infrastructure LiDARs. Preliminary findings indicate that when working with a tight sensing budget, it is possible that the incremental benefit derived from integrating additional low-resolution LiDARs could surpass that of incorporating more high-resolution ones. The results reinforce the necessity of investigating the cost-performance tradeoff.

翻译：基础设施集体感知技术通过实时共享与融合不同路侧传感器的检测数据实现目标检测，在提升交通安全与效率、消除遮挡方面展现出潜力。然而，该技术的应用受到路侧传感器布局缺乏指导以及事后评估成本高昂的制约。对于预算有限的基础设施项目而言，通过事前评估优化基础设施传感器的配置与布局，对于以较低成本最小化遮挡风险至关重要。本文提出支持基础设施传感器部署中集体感知成本-性能权衡事前评估的算法与仿真工具。具体而言，基础设施传感器部署被建模为整数规划问题，该问题可在多项式时间内高效求解，通过采用特定启发式算法获得接近最优的结果。相关解决方案可为确定传感器位置、安装高度及配置提供指导，从而在采购成本、安装物理约束与感知覆盖范围之间实现平衡。此外，我们在仿真引擎中实现了所提算法，从而能够通过目标检测视角评估各传感器部署方案的有效性。通过使用基础设施激光雷达进行交通监测的案例研究，展示了所提方法的应用效果。初步结果表明，在感知预算紧张的情况下，集成额外低分辨率激光雷达所带来的增量收益可能超过部署更多高分辨率激光雷达的收益。该结果进一步验证了研究成本-性能权衡的必要性。