  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.
