CoInfra: A Large-Scale Cooperative Infrastructure Perception System and Dataset for Vehicle-Infrastructure Cooperation in Adverse Weather
Minghao Ning, Yufeng Yang, Keqi Shu, Shucheng Huang, Jiaming Zhong, Maryam Salehi, Mahdi Rahmani, Jiaming Guo, Yukun Lu, Chen Sun, Aladdin Saleh, Ehsan Hashemi, Amir Khajepour

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…
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Taxonomy
TopicsAnomaly Detection Techniques and Applications
