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

arXiv: https://arxiv.org/abs/2405.14674 · HTML: https://mlsanity.com/paper/2405.14674/

## Key facts

- Cited by 12 papers in the map (data through 14 Oct 2026).
- Builds on 33 of its 47 references that are in the map.
- First posted on arXiv 23 May 2024.

## Builds on (most cited first, 30 of 33)

- [EfficientNet: Rethinking Model Scaling for Convolutional Neural Networks](https://mlsanity.com/paper/1905.11946/) (2019)
- [nuScenes: A multimodal dataset for autonomous driving](https://mlsanity.com/paper/1903.11027/) (2019)
- [CARLA: An Open Urban Driving Simulator](https://mlsanity.com/paper/1711.03938/) (2017)
- [Lift, Splat, Shoot: Encoding Images From Arbitrary Camera Rigs by Implicitly Unprojecting to 3D](https://mlsanity.com/paper/2008.05711/) (2020)
- [BEVDet: High-performance Multi-camera 3D Object Detection in Bird-Eye-View](https://mlsanity.com/paper/2112.11790/) (2021)
- [BEVDepth: Acquisition of Reliable Depth for Multi-view 3D Object Detection](https://mlsanity.com/paper/2206.10092/) (2022)
- [BEVDet4D: Exploit Temporal Cues in Multi-camera 3D Object Detection](https://mlsanity.com/paper/2203.17054/) (2022)
- [V2X-ViT: Vehicle-to-Everything Cooperative Perception with Vision Transformer](https://mlsanity.com/paper/2203.10638/) (2022)
- [V2VNet: Vehicle-to-Vehicle Communication for Joint Perception and Prediction](https://mlsanity.com/paper/2008.07519/) (2020)
- [Where2comm: Communication-Efficient Collaborative Perception via Spatial Confidence Maps](https://mlsanity.com/paper/2209.12836/) (2022)
- [FIERY: Future Instance Prediction in Bird's-Eye View from Surround Monocular Cameras](https://mlsanity.com/paper/2104.10490/) (2021)
- [R3Det: Refined Single-Stage Detector with Feature Refinement for Rotating Object](https://mlsanity.com/paper/1908.05612/) (2019)
- [Learning Distilled Collaboration Graph for Multi-Agent Perception](https://mlsanity.com/paper/2111.00643/) (2021)
- [Long-term Human Motion Prediction with Scene Context](https://mlsanity.com/paper/2007.03672/) (2020)
- [Predicting Semantic Map Representations from Images using Pyramid Occupancy Networks](https://mlsanity.com/paper/2003.13402/) (2020)
- [When2com: Multi-Agent Perception via Communication Graph Grouping](https://mlsanity.com/paper/2006.00176/) (2020)
- [PolarFormer: Multi-camera 3D Object Detection with Polar Transformer](https://mlsanity.com/paper/2206.15398/) (2022)
- [Who2com: Collaborative Perception via Learnable Handshake Communication](https://mlsanity.com/paper/2003.09575/) (2020)
- [Delving into the Devils of Bird's-eye-view Perception: A Review, Evaluation and Recipe](https://mlsanity.com/paper/2209.05324/) (2022)
- [COOPERNAUT: End-to-End Driving with Cooperative Perception for Networked Vehicles](https://mlsanity.com/paper/2205.02222/) (2022)
- [Collaborative Perception in Autonomous Driving: Methods, Datasets and Challenges](https://mlsanity.com/paper/2301.06262/) (2023)
- [Collaboration Helps Camera Overtake LiDAR in 3D Detection](https://mlsanity.com/paper/2303.13560/) (2023)
- [DeepAccident: A Motion and Accident Prediction Benchmark for V2X Autonomous Driving](https://mlsanity.com/paper/2304.01168/) (2023)
- [Spatio-Temporal Domain Awareness for Multi-Agent Collaborative Perception](https://mlsanity.com/paper/2307.13929/) (2023)
- [CORE: Cooperative Reconstruction for Multi-Agent Perception](https://mlsanity.com/paper/2307.11514/) (2023)
- [UMC: A Unified Bandwidth-efficient and Multi-resolution based Collaborative Perception Framework](https://mlsanity.com/paper/2303.12400/) (2023)
- [SGFormer: Semantic Graph Transformer for Point Cloud-based 3D Scene Graph Generation](https://mlsanity.com/paper/2303.11048/) (2023)
- [CMP: Cooperative Motion Prediction with Multi-Agent Communication](https://mlsanity.com/paper/2403.17916/) (2024)
- [PowerBEV: A Powerful Yet Lightweight Framework for Instance Prediction in Bird's-Eye View](https://mlsanity.com/paper/2306.10761/) (2023)
- [Aerial Monocular 3D Object Detection](https://mlsanity.com/paper/2208.03974/) (2022)

## Built on by (most cited first, 12 of 12)

- [T2SG: Traffic Topology Scene Graph for Topology Reasoning in Autonomous Driving](https://mlsanity.com/paper/2411.18894/) (2024)
- [Griffin: Aerial-Ground Cooperative Detection and Tracking Dataset and Benchmark](https://mlsanity.com/paper/2503.06983/) (2025)
- [OccuFly: A 3D Vision Benchmark for Semantic Scene Completion from the Aerial Perspective](https://mlsanity.com/paper/2512.20770/) (2025)
- [Is Intermediate Fusion All You Need for UAV-based Collaborative Perception?](https://mlsanity.com/paper/2504.21774/) (2025)
- [Long-SCOPE: Fully Sparse Long-Range Cooperative 3D Perception](https://mlsanity.com/paper/2604.09206/) (2026)
- [Towards Large Model Feature Coding](https://mlsanity.com/paper/2605.24025/) (2026)
- [Refine-and-Contrast: Adaptive Instance-Aware BEV Representations for Multi-UAV Collaborative Object Detection](https://mlsanity.com/paper/2508.12684/) (2025)
- [Leveraging Large Vision Model for Multi-UAV Co-perception in Low-Altitude Wireless Networks](https://mlsanity.com/paper/2603.16927/) (2026)
- [UAV-CB: A Complex-Background RGB-T Dataset and Local Frequency Bridge Network for UAV Detection](https://mlsanity.com/paper/2603.17492/) (2026)
- [AeroVerse-SatAgent: UAV-Satellite Collaborative Spatial Reasoning Inspired by the Dual Visual Pathway Theory of Cognitive Neuroscience](https://mlsanity.com/paper/2606.31467/) (2026)
- [DAPM: UAV Monocular Depth Estimation from Any Height, Pitch, Roll and FOV](https://mlsanity.com/paper/2607.21438/) (2026)
- [CERF: Communication-Efficient and Retraining-Free Collaborative Perception](https://mlsanity.com/paper/2609.00951/) (2026)

## Abstract (arXiv)

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.