用无人机替代固定基站，实现车与空基设备协同感知。
AirV2X: Unified Air-Ground Vehicle-to-Everything Collaboration
- 以无人机作为移动感知节点，弥补地面设施覆盖盲区。
- 构建6.73小时多环境车机-无人机协同数据集。
- 适合研究空地协同自动驾驶的开发者和科研人员。
多车协同驾驶相比单车自主具有明显优势，但传统基于基础设施的车联网系统受限于高昂部署成本，且在城乡结合部和偏远地区存在
原文摘要 · Abstract (English)
While multi-vehicular collaborative driving demonstrates clear advantages over single-vehicle autonomy, traditional infrastructure-based V2X systems remain constrained by substantial deployment costs and the creation of "uncovered danger zones" in rural and suburban areas. We present AirV2X-Perception, a large-scale dataset that leverages Unmanned Aerial Vehicles (UAVs) as a flexible alternative or complement to fixed Road-Side Units (RSUs). Drones offer unique advantages over ground-based perception: complementary bird's-eye-views that reduce occlusions, dynamic positioning capabilities that enable hovering, patrolling, and escorting navigation rules, and significantly lower deployment costs compared to fixed infrastructure. Our dataset comprises 6.73 hours of drone-assisted driving scenarios across urban, suburban, and rural environments with varied weather and lighting conditions. The AirV2X-Perception dataset facilitates the development and standardized evaluation of Vehicle-to-Drone (V2D) algorithms, addressing a critical gap in the rapidly expanding field of aerial-assisted autonomous driving systems. The dataset and development kits are open-sourced at https://github.com/taco-group/AirV2X-Perception.
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