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

Welcome to the official PyTorch implementation of "Drones Help Drones: A Collaborative Framework for Multi-Drone Object Trajectory Prediction and Beyond." We have open-sourced this repository to foster research and collaboration in the field of multi-drone trajectory prediction and related areas.

Code Availability

The implementation code is now available.

Latest News

"Drones Help Drones" has been accepted as a Poster at the Thirty-eighth Annual Conference on Neural Information Processing Systems (NeurIPS 2024). You can access the paper on arXiv.

Setup Instructions

Step 1: Create the Conda Environment

To set up the environment, use the following command:

conda env create -f environment.yml

Step 2: Replace splits.py

Ensure you replace the splits.py file in the nuscenes package (typically found at /miniconda3/envs/dhd/lib/python3.7/site-packages/nuscenes/utils/splits.py) with our provided version of splits.py.

Step 3: Download the Dataset

Download the complete Air-Co-Pred dataset, which includes the Trainval dataset (metadata and file blobs parts 0-36), from the following link:

Download Link
Access Code: 4av8

Once downloaded, extract the .tar files into your desired data root directory (YOUR_DATAROOT), organizing them as follows:

Air-Co-Pred/
├── trainval/
│   ├── maps/
│   ├── samples/
│   ├── sweeps/
│   └── v1.0-trainval/

Model Training

To train the DHD (Drones Help Drones) model, execute the following command:

python train.py --config=dhd/config/dhd.yml \
                LOG_DIR xxx \
                GPUS [x,x,x,x] \
                BATCHSIZE 1 \
                DATASET.DATAROOT YOUR_DATAROOT

Model Evaluation

To evaluate the model with trained weights, run:

python test.py --config dhd/config/dhd.yml \
                PRETRAINED.LOAD_WEIGHTS True \
                PRETRAINED.PATH $YOUR_PRETRAINED_WEIGHTS_PATH \
                GPUS [x,x,x,x] \
                BATCHSIZE 1 \
                DATASET.DATAROOT YOUR_DATAROOT

Citation

If you find this work helpful in your research, please consider citing us:

@inproceedings{
  title={Drones Help Drones: A Collaborative Framework for Multi-Drone Object Trajectory Prediction and Beyond},
  author={Wang Z, Cheng P, Chen M, Tian P, Wang Z, Li X, Yang X, Sun X.},
  booktitle={The Thirty-eighth Annual Conference on Neural Information Processing Systems},
  year={2024}
}

@misc{wang2024droneshelpdronescollaborative,
      title={Drones Help Drones: A Collaborative Framework for Multi-Drone Object Trajectory Prediction and Beyond}, 
      author={Zhechao Wang and Peirui Cheng and Mingxin Chen and Pengju Tian and Zhirui Wang and Xinming Li and Xue Yang and Xian Sun},
      year={2024},
      eprint={2405.14674},
      archivePrefix={arXiv},
      primaryClass={cs.CV},
      url={https://arxiv.org/abs/2405.14674}, 
}