Drones Help Drones: A Collaborative Framework for Multi-Drone Object Trajectory Prediction and Beyond

TL;DR

This paper introduces 'Drones Help Drones', a collaborative framework that improves multi-drone object trajectory prediction by enhancing BEV accuracy and reducing communication load, validated on a new dataset with significant performance gains.

Contribution

The paper presents a novel framework for multi-drone collaboration that incorporates ground priors and a selective interaction mechanism, along with a new dataset for evaluation.

Findings

Reduces BEV position deviation by over 20%.

Requires only a quarter of the communication bandwidth.

Achieves comparable prediction accuracy to state-of-the-art methods.

Abstract

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.