CoInfra: A Large-Scale Cooperative Infrastructure Perception System and Dataset for Vehicle-Infrastructure Cooperation in Adverse Weather

TL;DR

CoInfra introduces a large-scale cooperative perception system and dataset for vehicle-infrastructure cooperation, demonstrating improved safety awareness in adverse weather and complex traffic scenarios using real 5G communication.

Contribution

The paper presents a deployable multi-node infrastructure perception platform, a comprehensive dataset under adverse weather, and evaluation of V2I cooperation benefits in safety-critical scenarios.

Findings

V2I cooperation increases critical-frame completeness from 33%-46% to 86%-100%.

The dataset includes 294k LiDAR frames and 589k camera images across diverse weather conditions.

Infrastructure sensing significantly enhances awareness of traffic participants in conflict scenarios.

Abstract

Vehicle-infrastructure (V2I) cooperative perception can substantially extend the range, coverage, and robustness of autonomous driving systems beyond the limits of onboard-only sensing, particularly in occluded and adverse-weather environments. However, its practical value is still difficult to quantify because existing benchmarks do not adequately capture large-scale multi-node deployments, realistic communication conditions, and adverse-weather operation. This paper presents CoInfra, a deployable cooperative infrastructure perception platform comprising 14 roadside sensor nodes connected through a commercial 5G network, together with a large-scale dataset and an open-source system stack for V2I cooperation research. The system supports synchronized multi-node sensing and delay-aware fusion under real 5G communication constraints. The released dataset covers an eight-node urban roundabout under four weather conditions (sunny, rainy, heavy snow, and freezing rain) and contains 294k LiDAR frames, 589k camera images, and 332k globally consistent 3D bounding boxes. It also includes a synchronized V2I subset collected with an autonomous vehicle. Beyond standard perception benchmarks, we further evaluate whether infrastructure sensing improves awareness of safety-critical traffic participants during roundabout interactions. In structured conflict scenarios, V2I cooperation increases critical-frame completeness from 33%-46% with vehicle-only sensing to 86%-100%. These results show that multi-node infrastructure perception can significantly improve situational awareness in conflict-rich traffic scenarios where vehicle-only sensing is most limited.