Recent advances in sensing and communication have paved the way for collective perception in traffic management, with real-time data sharing among multiple entities. While vehicle-based collective perception has gained traction, infrastructure-based approaches, which entail the real-time sharing and merging of sensing data from different roadside sensors for object detection, grapple with challenges in placement strategy and high ex-post evaluation costs. Despite anecdotal evidence of their effectiveness, many current deployments rely on engineering heuristics and face budget constraints that limit post-deployment adjustments. This paper introduces polynomial-time heuristic algorithms and a simulation tool for the ex-ante evaluation of infrastructure sensor deployment. By modeling it as an integer programming problem, we guide decisions on sensor locations, heights, and configurations to harmonize cost, installation constraints, and coverage. Our simulation engine, integrated with open-source urban driving simulators, enables us to evaluate the effectiveness of each sensor deployment solution through the lens of object detection. A case study with infrastructure LiDARs revealed 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 prior to deployment. The code for our simulation experiments can be found at https://github.com/dajiangsuo/SEIP.

翻译：传感与通信领域的最新进展为交通管理中的协同感知（即多实体间的实时数据共享）铺平了道路。尽管基于车辆的协同感知已得到广泛应用，但基于基础设施的协同感知方法（即实时共享与融合来自不同路侧传感器的感知数据以进行目标检测）仍面临部署策略优化及事后评估成本高昂的挑战。尽管此类方法的有效性已有经验性证据，但多数现有部署依赖工程启发性策略，且受限于预算约束而难以在部署后进行调整。本文提出多项式时间启发式算法及仿真工具，用于基础设施传感器部署的事前评估。通过将问题建模为整数规划模型，我们指导传感器位置、高度及配置的决策，以协调成本、安装约束与覆盖范围。我们的仿真引擎与开源城市驾驶模拟器集成，能够通过目标检测视角评估每种传感器部署方案的有效性。基于基础设施激光雷达的案例研究表明：集成额外低分辨率激光雷达所获得的增量收益可能超越添加更多高分辨率激光雷达的效果。该结果进一步论证了在部署前研究成本-性能权衡的必要性。仿真实验代码详见 https://github.com/dajiangsuo/SEIP。