{"id":20731457,"url":"https://github.com/opendrivelab/hdgt","last_synced_at":"2025-07-11T04:07:44.418Z","repository":{"id":107761830,"uuid":"491886974","full_name":"OpenDriveLab/HDGT","owner":"OpenDriveLab","description":"[IEEE T-PAMI 2023] Unified heterogeneous transformer-based graph neural network for motion prediction","archived":false,"fork":false,"pushed_at":"2023-12-06T13:11:23.000Z","size":1265,"stargazers_count":120,"open_issues_count":1,"forks_count":10,"subscribers_count":12,"default_branch":"main","last_synced_at":"2025-06-30T22:08:12.835Z","etag":null,"topics":["autonomous-driving","motion-prediction","waymo-challenge"],"latest_commit_sha":null,"homepage":"https://ieeexplore.ieee.org/document/10192373","language":"Python","has_issues":true,"has_wiki":null,"has_pages":null,"mirror_url":null,"source_name":null,"license":null,"status":null,"scm":"git","pull_requests_enabled":true,"icon_url":"https://github.com/OpenDriveLab.png","metadata":{"files":{"readme":"README.md","changelog":null,"contributing":null,"funding":".github/FUNDING.yml","license":null,"code_of_conduct":null,"threat_model":null,"audit":null,"citation":null,"codeowners":null,"security":null,"support":null,"governance":null},"funding":{"github":["OpenDriveLab"],"patreon":null,"open_collective":null,"ko_fi":null,"tidelift":null,"community_bridge":null,"liberapay":null,"issuehunt":null,"otechie":null,"lfx_crowdfunding":null,"custom":null}},"created_at":"2022-05-13T12:22:30.000Z","updated_at":"2025-06-17T10:41:51.000Z","dependencies_parsed_at":"2023-12-06T14:29:46.093Z","dependency_job_id":"271caa47-1657-41e4-b7b5-e15fe0365a48","html_url":"https://github.com/OpenDriveLab/HDGT","commit_stats":null,"previous_names":["opendrivelab/hdgt"],"tags_count":0,"template":false,"template_full_name":null,"purl":"pkg:github/OpenDriveLab/HDGT","repository_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/OpenDriveLab%2FHDGT","tags_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/OpenDriveLab%2FHDGT/tags","releases_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/OpenDriveLab%2FHDGT/releases","manifests_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/OpenDriveLab%2FHDGT/manifests","owner_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/owners/OpenDriveLab","download_url":"https://codeload.github.com/OpenDriveLab/HDGT/tar.gz/refs/heads/main","sbom_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/OpenDriveLab%2FHDGT/sbom","host":{"name":"GitHub","url":"https://github.com","kind":"github","repositories_count":264726769,"owners_count":23654494,"icon_url":"https://github.com/github.png","version":null,"created_at":"2022-05-30T11:31:42.601Z","updated_at":"2022-07-04T15:15:14.044Z","host_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub","repositories_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories","repository_names_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repository_names","owners_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/owners"}},"keywords":["autonomous-driving","motion-prediction","waymo-challenge"],"created_at":"2024-11-17T05:14:55.042Z","updated_at":"2025-07-11T04:07:44.410Z","avatar_url":"https://github.com/OpenDriveLab.png","language":"Python","funding_links":["https://github.com/sponsors/OpenDriveLab"],"categories":[],"sub_categories":[],"readme":"\u003e [!IMPORTANT]\n\u003e 🌟 Stay up to date at [opendrivelab.com](https://opendrivelab.com/#news)!\n\n# HDGT: Modeling the Driving Scene with Heterogenity and Relativity\n\n\u003e **HDGT: Heterogeneous Driving Graph Transformer for Multi-Agent Trajectory Prediction via Scene Encoding** [IEEE TPAMI 2023] \n\u003e![pipeline](src/pipeline.PNG)\n\u003e - [Paper](http://arxiv.org/abs/2205.09753)\n\n## Introduction\n\nHDGT 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.\n\nBy 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. \n\n## Getting Started\n\n- [Installation](docs/INSTALL.md)\n- [Prepare Dataset](docs/DATA_PREP.md)\n- [Train \u0026 Evaluation](docs/TRAIN_EVAL.md)\n\n\n## License\n\nAll assets and code are under the [Apache 2.0 license](./LICENSE) unless specified otherwise.\n\n## Bibtex\nIf this work is helpful for your research, please consider citing the following BibTeX entry.\n\n```\n@article{jia2023hdgt,\n  title={HDGT: Heterogeneous Driving Graph Transformer for Multi-Agent Trajectory Prediction via Scene Encoding},\n  author={Jia, Xiaosong and Wu, Penghao and Chen, Li and  Liu, Yu  and Li, Hongyang and Yan, Junchi},\n  journal = {IEEE Transactions on Pattern Analysis and Machine Intelligence (TPAMI)},\n  year = {2023},\n}  \n```\n\n```\n@inproceedings{jia2022temporal,\n  title={Towards Capturing the Temporal Dynamics for Trajectory Prediction: a Coarse-to-Fine Approach},\n  author={Jia, Xiaosong and Chen, Li and Wu, Penghao and Zeng, Jia and  Yan, Junchi and Li, Hongyang and Qiao, Yu},\n  booktitle={CoRL},\n  year={2022}\n} \n```\n\n\n","project_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fopendrivelab%2Fhdgt","html_url":"https://awesome.ecosyste.ms/projects/github.com%2Fopendrivelab%2Fhdgt","lists_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fopendrivelab%2Fhdgt/lists"}