https://github.com/opendrivelab/hdgt
[IEEE T-PAMI 2023] Unified heterogeneous transformer-based graph neural network for motion prediction
https://github.com/opendrivelab/hdgt
autonomous-driving motion-prediction waymo-challenge
Last synced: 27 days ago
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[IEEE T-PAMI 2023] Unified heterogeneous transformer-based graph neural network for motion prediction
- Host: GitHub
- URL: https://github.com/opendrivelab/hdgt
- Owner: OpenDriveLab
- Created: 2022-05-13T12:22:30.000Z (about 3 years ago)
- Default Branch: main
- Last Pushed: 2023-12-06T13:11:23.000Z (over 1 year ago)
- Last Synced: 2025-04-23T22:05:01.764Z (27 days ago)
- Topics: autonomous-driving, motion-prediction, waymo-challenge
- Language: Python
- Homepage: https://ieeexplore.ieee.org/document/10192373
- Size: 1.21 MB
- Stars: 121
- Watchers: 12
- Forks: 9
- Open Issues: 1
-
Metadata Files:
- Readme: README.md
- Funding: .github/FUNDING.yml
Awesome Lists containing this project
README
# HDGT: Modeling the Driving Scene with Heterogenity and Relativity
> **HDGT: Heterogeneous Driving Graph Transformer for Multi-Agent Trajectory Prediction via Scene Encoding** [IEEE TPAMI 2023]
>
> - [Paper](http://arxiv.org/abs/2205.09753)
## Introduction
HDGT is an unified heterogeneous transformer-based graph neural network for driving scene encoding. It is a **SOTA method** on [INTERACTION](http://challenge.interaction-dataset.com/leader-board) and [Waymo](https://waymo.com/open/challenges/2021/motion-prediction/) Motion Prediction Chanllege.
By time of release in April 2022, the proposed method achieves new state-of-the-art on INTERACTION Prediction Challenge and Waymo Open Motion Challenge, in which we rank the **first** and **second** respectively in terms of the minADE/minFDE metric.
## Getting Started
- [Installation](docs/INSTALL.md)
- [Prepare Dataset](docs/DATA_PREP.md)
- [Train & Evaluation](docs/TRAIN_EVAL.md)
## License
All assets and code are under the [Apache 2.0 license](./LICENSE) unless specified otherwise.
## Bibtex
If this work is helpful for your research, please consider citing the following BibTeX entry.
```
@article{jia2023hdgt,
title={HDGT: Heterogeneous Driving Graph Transformer for Multi-Agent Trajectory Prediction via Scene Encoding},
author={Jia, Xiaosong and Wu, Penghao and Chen, Li and Liu, Yu and Li, Hongyang and Yan, Junchi},
journal = {IEEE Transactions on Pattern Analysis and Machine Intelligence (TPAMI)},
year = {2023},
}
```
```
@inproceedings{jia2022temporal,
title={Towards Capturing the Temporal Dynamics for Trajectory Prediction: a Coarse-to-Fine Approach},
author={Jia, Xiaosong and Chen, Li and Wu, Penghao and Zeng, Jia and Yan, Junchi and Li, Hongyang and Qiao, Yu},
booktitle={CoRL},
year={2022}
}
```