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arXiv · 2603.25275

V2U4Real: A Real-world Large-scale Dataset for Vehicle-to-UAV Cooperative Perception

Weijia Li·Haoen Xiang·Tianxu Wang·Shuaibing Wu·Qiming Xia·Cheng Wang·Chenglu Wen
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Abstract

Modern autonomous vehicle perception systems are often constrained by occlusions, blind spots, and limited sensing range. While existing cooperative perception paradigms, such as Vehicle-to-Vehicle (V2V) and Vehicle-to-Infrastructure (V2I), have demonstrated their effectiveness in mitigating these challenges, they remain limited to ground-level collaboration and cannot fully address large-scale occlusions or long-range perception in complex environments. To advance research in cross-view cooperative perception, we present V2U4Real, the first large-scale real-world multi-modal dataset for Vehicle-to-UAV (V2U) cooperative object perception. V2U4Real is collected by a ground vehicle and a UAV equipped with multi-view LiDARs and RGB cameras. The dataset covers urban streets, university campuses, and rural roads under diverse traffic scenarios, comprising over 56K LiDAR frames, 56K multi-view camera images, and 700K annotated 3D bounding boxes across four classes. To support a wide range of research tasks, we establish benchmarks for single-agent 3D object detection, cooperative 3D object detection, and object tracking. Comprehensive evaluations of several state-of-the-art models demonstrate the effectiveness of V2U cooperation in enhancing perception robustness and long-range awareness. The V2U4Real dataset and codebase is available at https://github.com/VjiaLi/V2U4Real.

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Weijia Li, Haoen Xiang, Tianxu Wang, Shuaibing Wu, Qiming Xia, Cheng Wang, Chenglu Wen. 2026-03-26. V2U4Real: A Real-world Large-scale Dataset for Vehicle-to-UAV Cooperative Perception. https://arxiv.org/abs/2603.25275

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