构建首个空地协同3D感知数据集，助力自动驾驶多车协作研究
Griffin: Aerial-Ground Cooperative Detection and Tracking Dataset and Benchmark
- 空地协同采集250+动态场景，覆盖20-60米无人机高度与多种天气
- 包含37000+帧带遮挡感知标注，支持通信效率与鲁棒性评估
- 适合作为自动驾驶、无人机协同系统研发的基准数据集
尽管协同感知可克服单车系统的局限性，但车车与车路协同系统常因高昂成本难以落地。空地协同（AGC）通过地面车辆与无人机配合，提供了更经济且快速部署的替代方案。然而该领域受限于高质量公开数据集和基准的缺失。为此，我们提出Griffin，一个全面的AGC 3D感知数据集，包含超过250个动态场景（37,000+帧），涵盖20-60米不同无人机高度、多样天气条件，通过CARLA-AirSim联合仿真实现真实无人机运动动态，并提供关键遮挡感知的3D标注。配套统一的基准框架支持协作检测与跟踪评估，涵盖通信效率、高度适应性及对通信延迟、数据丢失与定位噪声的鲁棒性测试。通过多种协同范式实验，验证了现有方法的有效性与局限性，为未来研究提供重要参考。数据集与代码已开源：https://github.com/wang-jh18-SVM/Griffin。
原文摘要 · Abstract (English)
While cooperative perception can overcome the limitations of single-vehicle systems, the practical implementation of vehicle-to-vehicle and vehicle-to-infrastructure systems is often impeded by significant economic barriers. Aerial-ground cooperation (AGC), which pairs ground vehicles with drones, presents a more economically viable and rapidly deployable alternative. However, this emerging field has been held back by a critical lack of high-quality public datasets and benchmarks. To bridge this gap, we present \textit{Griffin}, a comprehensive AGC 3D perception dataset, featuring over 250 dynamic scenes (37k+ frames). It incorporates varied drone altitudes (20-60m), diverse weather conditions, realistic drone dynamics via CARLA-AirSim co-simulation, and critical occlusion-aware 3D annotations. Accompanying the dataset is a unified benchmarking framework for cooperative detection and tracking, with protocols to evaluate communication efficiency, altitude adaptability, and robustness to communication latency, data loss and localization noise. By experiments through different cooperative paradigms, we demonstrate the effectiveness and limitations of current methods and provide crucial insights for future research. The dataset and codes are available at https://github.com/wang-jh18-SVM/Griffin.
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