HPNet: Dynamic Trajectory Forecasting with Historical Prediction Attention

arXiv | paper | poster
This repository is the official implementation of HPNet: Dynamic Trajectory Forecasting with Historical Prediction Attention published in CVPR 2024.

Table of Contents

• Setup
• Datasets
• Training
• Validation
• Testing
• Pre-trained Models & Results
• Acknowledgements
• Citation

Setup

Clone the repository and set up the environment:

git clone https://github.com/XiaolongTang23/HPNet.git
cd HPNet
conda create -n HPNet python=3.8
conda activate HPNet
conda install pytorch==2.1.0 torchvision==0.16.0 torchaudio==2.1.0 pytorch-cuda=12.1 -c pytorch -c nvidia
pip install torch_geometric==2.3.1
conda install pytorch-lightning==2.0.3

Note: For compatibility, you may experiment with different versions, e.g., PyTorch 1.12.1 has been confirmed to work.

Datasets

/path/to/Argoverse_root/
├── train/
│   └── data/
│       ├── 1.csv
│       ├── 2.csv
│       ├── ...
└── val/
    └── data/
        ├── 1.csv
        ├── 2.csv
        ├── ...

/path/to/INTERACTION_root/
├── maps/
├── test_conditional-multi-agent/
├── test_multi-agent/
├── train/
│   ├── DR_CHN_Merging_ZS0_train
│   ├── ...
└── val/
    ├── DR_CHN_Merging_ZS0_val
    ├── ...

pip install lanelet2==1.2.1

Training

Data preprocessing may take several hours the first time you run this project. Training on 8 RTX 4090 GPUs, one epoch takes about 30 and 6 minutes for Argoverse and INTERACTION, respectively.

# For Argoverse
python HPNet-Argoverse/train.py --root /path/to/Argoverse_root/ --train_batch_size 2 --val_batch_size 2 --devices 8

# For INTERACTION
python HPNet-INTERACTION/train.py --root /path/to/INTERACTION_root/ --train_batch_size 2 --val_batch_size 2 --devices 8

Validation

# For Argoverse
python HPNet-Argoverse/val.py --root /path/to/Argoverse_root/ --val_batch_size 2 --devices 8 --ckpt_path /path/to/checkpoint.ckpt

# For INTERACTION
python HPNet-INTERACTION/val.py --root /path/to/INTERACTION_root/ --val_batch_size 2 --devices 8 --ckpt_path /path/to/checkpoint.ckpt

Testing

# For Argoverse
python HPNet-Argoverse/test.py --root /path/to/Argoverse_root/ --test_batch_size 2 --devices 1 --ckpt_path /path/to/checkpoint.ckpt

# For INTERACTION
python HPNet-INTERACTION/test.py --root /path/to/INTERACTION_root/ --test_batch_size 2 --devices 1 --ckpt_path /path/to/checkpoint.ckpt

Pre-trained Models & Results

Argoverse

• Pre-trained model: Download here
• Performance Metrics:

INTERACTION

• Pre-trained model: Download here
• Performance Metrics:

Acknowledgements

We sincerely appreciate Argoverse, INTERACTION,QCNet and HiVT for their awesome codebases.

Citation

If HPNet has been helpful in your research, please consider citing our work:

@inproceedings{tang2024hpnet,
  title={Hpnet: Dynamic trajectory forecasting with historical prediction attention},
  author={Tang, Xiaolong and Kan, Meina and Shan, Shiguang and Ji, Zhilong and Bai, Jinfeng and Chen, Xilin},
  booktitle={Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition},
  pages={15261--15270},
  year={2024}
}