DGFNet: Multi-Agent Trajectory Prediction with Difficulty-Guided Feature Enhancement Network

Paper | Webpage

This is the official implementation of the paper Multi-Agent Trajectory Prediction with Difficulty-Guided Feature Enhancement Network.

Introduction

Argoverse 1(single model)

• 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	-

Argoverse 1(ensemble model)

• Performance Metrics:

Split	brier-minFDE	minFDE	MR	minADE
Test	1.693	1.110	0.107	0.752

Qualitative Results

• On Argoverse 1 motion forecasting dataset

• On Argoverse 2 motion forecasting dataset

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Gettting Started

Install dependencies

• 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

Train from scratch

• 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

How to Cite

@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}}

Acknowledgment

We would like to express sincere thanks to the authors of the following packages and tools:

• Simpl
• ADAPT

License

This repository is licensed under MIT license.