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

Source: [Drones Help Drones: A Collaborative Framework for Multi-Drone Object Trajectory Prediction and Beyond](<https://www.sota2.com/research/paper/68fa73451e209131cea5b730>)

Collection index: [llms.txt](<https://www.sota2.com/research/llms.txt>)

Data updated: 2025-10-26T02:46:17.278Z

Authors: Zhechao Wang, Peirui Cheng, Mingxin Chen, Pengju Tian, Zhirui Wang, Xinming Li, Xue Yang, Xian Sun

Published: 2024-05-23T15:11:23.000Z

## 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.

## Recorded benchmark results

A research result applies to its stated dataset, split, metric and evaluation configuration. Compare like-for-like settings, preserve the metric's unit and direction, and distinguish paper-reported results from independent reproduction. The best result recorded here is not a claim of exhaustive, current global coverage.

- [Cooperative Perception on Air-Co-Pred Short Long Avg](<https://www.sota2.com/research/sota/cooperative-perception-on-air-co-pred-short-long-avg.md>): Cooperative Perception; Air-Co-Pred Short Long Avg; IoU (Short): 60.58.

## Original sources and implementation

[Original paper](<https://arxiv.org/abs/2405.14674>)

[PDF](<https://arxiv.org/pdf/2405.14674.pdf>)