Collaborative trajectory prediction can comprehensively forecast the future motion of objects through multi-view complementary information. However, it encounters two main challenges in multi-drone collaboration settings. The expansive aerial observations make it difficult to generate precise Bird's Eye View (BEV) representations. Besides, excessive interactions can not meet real-time prediction requirements within the constrained drone-based communication bandwidth. To address these problems, we propose a novel framework named "Drones Help Drones" (DHD). Firstly, we incorporate the ground priors provided by the drone's inclined observation to estimate the distance between objects and drones, leading to more precise BEV generation. Secondly, we design a selective mechanism based on the local feature discrepancy to prioritize the critical information contributing to prediction tasks during inter-drone interactions. Additionally, we create the first dataset for multi-drone collaborative prediction, named "Air-Co-Pred", and conduct quantitative and qualitative experiments to validate the effectiveness of our DHD framework.The results demonstrate that compared to state-of-the-art approaches, DHD reduces position deviation in BEV representations by over 20% and requires only a quarter of the transmission ratio for interactions while achieving comparable prediction performance. Moreover, DHD also shows promising generalization to the collaborative 3D object detection in CoPerception-UAVs.

翻译：协同轨迹预测能够通过多视角互补信息全面预测目标的未来运动。然而，在多无人机协同场景中，该方法面临两大挑战。广阔的空中观测使得生成精确的鸟瞰图表示变得困难。此外，在受限的无人机通信带宽内，过度的交互无法满足实时预测需求。为解决这些问题，我们提出了一种名为“无人机协作无人机”的新框架。首先，我们利用无人机倾斜观测提供的地面先验信息来估计目标与无人机之间的距离，从而实现更精确的鸟瞰图生成。其次，我们设计了一种基于局部特征差异的选择性机制，以在无人机间交互过程中优先处理对预测任务至关重要的信息。此外，我们创建了首个面向多无人机协同预测的数据集“Air-Co-Pred”，并通过定量与定性实验验证了DHD框架的有效性。实验结果表明，与现有先进方法相比，DHD将鸟瞰图表示中的位置偏差降低了超过20%，在达到相当预测性能的同时仅需四分之一的交互传输比例。此外，DHD在CoPerception-UAVs的协同三维目标检测任务中也展现出良好的泛化能力。