  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.
