This is the official implementation of the paper Multi-Agent Trajectory Prediction with Difficulty-Guided Feature Enhancement Network.
This paper is currently being submitted to IEEE Robotics and Automation Letters (RA-L).
- Performance Metrics:
Split | brier-minFDE | minFDE | MR | minADE | Param |
---|---|---|---|---|---|
Val | 1.499 | 0.897 | 0.073 | 0.634 | 4.53 |
Test | 1.742 | 1.117 | 0.108 | 0.763 | - |
- Performance Metrics:
Split | brier-minFDE | minFDE | MR | minADE |
---|---|---|---|---|
Test | 1.693 | 1.110 | 0.107 | 0.752 |
- On Argoverse 1 motion forecasting dataset
- On Argoverse 2 motion forecasting dataset
- Create a new conda virtual env
conda create --name DGFNet python=3.8
conda activate DGFNet
- Install PyTorch according to your CUDA version. We recommend CUDA >= 11.1, PyTorch >= 1.8.0.
conda install pytorch==1.12.0 torchvision==0.13.0 torchaudio==0.12.0 cudatoolkit=11.6 -c pytorch -c conda-forge
-
Install Argoverse 1 APIs, please follow argoverse-api.
-
Install other dependencies
pip install scikit-image IPython tqdm ipdb tensorboard
- Preprocess full Argoverse 1 motion forecasting dataset using the script:
sh scripts/argo_preproc.sh
- Launch training using the script:
sh scripts/DGFNet_train.sh
- For model evaluation, please refer to the following scripts:
sh scripts/DGFNet_eval.sh
@article{xin2024multi,
title={Multi-Agent Trajectory Prediction with Difficulty-Guided Feature Enhancement Network},
author={Xin, Guipeng and Chu, Duanfeng and Lu, Liping and Deng, Zejian and Lu, Yuang and Wu, Xigang},
journal={arXiv preprint arXiv:2407.18551},
year={2024}}
We would like to express sincere thanks to the authors of the following packages and tools:
This repository is licensed under MIT license.