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

TL;DR
V2U4Real is a large-scale, real-world multi-modal dataset designed for Vehicle-to-UAV cooperative perception, enabling research on overcoming occlusions and enhancing long-range perception in complex environments.
Contribution
This paper introduces V2U4Real, the first extensive real-world dataset for V2U cooperative perception with multi-view sensors, covering diverse scenarios and establishing benchmarks for multiple perception tasks.
Findings
V2U cooperation improves perception robustness.
The dataset enables effective long-range object detection.
State-of-the-art models show significant gains with V2U data.
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…
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Taxonomy
TopicsAdvanced Neural Network Applications · Autonomous Vehicle Technology and Safety · Adversarial Robustness in Machine Learning
