# Cooperative Infrastructure Perception

arXiv: https://arxiv.org/abs/2207.08930 · HTML: https://mlsanity.com/paper/2207.08930/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 18 Jul 2022.

## Built on by (most cited first, 1 of 1)

- [RESOLVE: A Multi-Resolution and Multi-Modal Dataset for Roadside Cooperative Perception](https://mlsanity.com/paper/2606.31895/) (2026)

## Abstract (arXiv)

Recent works have considered two qualitatively different approaches to overcome line-of-sight limitations of 3D sensors used for perception: cooperative perception and infrastructure-augmented perception. In this paper, motivated by increasing deployments of infrastructure LiDARs, we explore a third approach, cooperative infrastructure perception. This approach generates perception outputs by fusing outputs of multiple infrastructure sensors, but, to be useful, must do so quickly and accurately. We describe the design, implementation and evaluation of Cooperative Infrastructure Perception (CIP), which uses a combination of novel algorithms and systems optimizations. It produces perception outputs within 100 ms using modest computing resources and with accuracy comparable to the state-of-the-art. CIP, when used to augment vehicle perception, can improve safety. When used in conjunction with offloaded planning, CIP can increase traffic throughput at intersections.