V2U4Real：面向车-无人机协同感知的真实世界大规模数据集
V2U4Real: A Real-world Large-scale Dataset for Vehicle-to-UAV Cooperative Perception
该数据集为跨视角协同感知提供了真实世界多模态基准，可用于研究车-无人机协同下的3D检测与跟踪，解决地面视角的遮挡与远距离感知问题。
原文依据 V2U4Real is collected by a ground vehicle and a UAV equipped with multi-view LiDARs and RGB cameras.
摘要译文
由模型翻译 · 术语统一 · 请对照原文核对关键结论现代自动驾驶车辆感知系统常受遮挡、盲区和有限感知范围制约。尽管现有的协同感知范式，如车-车（V2V）和车-基础设施（V2I），已证明能有效缓解这些挑战，但它们仍局限于地面层面的协作，无法完全解决复杂环境中的大规模遮挡或远距离感知问题。为推动跨视角协同感知研究，我们提出V2U4Real，这是首个面向车-无人机（V2U）协同目标感知的大规模真实世界多模态数据集。V2U4Real由一辆地面车辆和一架搭载多视角激光雷达与RGB相机的无人机组采集。该数据集覆盖城市街道、大学校园和乡村道路等多种交通场景，包含超过5.6万帧激光雷达数据、5.6万张多视角相机图像以及70万个标注的3D边界框，涵盖四个类别。为支持广泛的研究任务，我们建立了单智能体3D目标检测、协同3D目标检测和目标跟踪的基准。对多个最优模型的综合评估表明，车-无人机协同在提升感知鲁棒性和远距离感知能力方面具有有效性。
英文原文摘要 1303 字符
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.