Roadside perception systems are increasingly crucial in enhancing traffic
safety and facilitating cooperative driving for autonomous vehicles. Despite
rapid technological advancements, a major challenge persists for this newly
arising field: the absence of standardized evaluation methods and benchmarks
for these systems. This limitation hampers the ability to effectively assess
and compare the performance of different systems, thus constraining progress in
this vital field. This paper introduces a comprehensive evaluation methodology
specifically designed to assess the performance of roadside perception systems.
Our methodology encompasses measurement techniques, metric selection, and
experimental trial design, all grounded in real-world field testing to ensure
the practical applicability of our approach.
We applied our methodology in Mcity\footnote{https://mcity.umich.edu/},
a controlled testing environment, to evaluate various off-the-shelf perception
systems. This approach allowed for an in-depth comparative analysis of their
performance in realistic scenarios, offering key insights into their respective
strengths and limitations. The findings of this study are poised to inform the
development of industry-standard benchmarks and evaluation methods, thereby
enhancing the effectiveness of roadside perception system development and
deployment for autonomous vehicles. We anticipate that this paper will
stimulate essential discourse on standardizing evaluation methods for roadside
perception systems, thus pushing the frontiers of this technology. Furthermore,
our results offer both academia and industry a comprehensive understanding of
the capabilities of contemporary infrastructure-based perception systems.

该研究提出了一种综合评估方法，旨在评估道路边缘感知系统的性能，通过采用实际场景测试，对不同系统的性能进行了深入比较分析，为标准化评估方法的发展提供了关键见解，并增强了自动驾驶车辆的道路感知系统开发和部署的效果。