https://github.com/opendrivelab/betop

[NeurIPS 2024] Behavioral Topology (BeTop), a multi-agent behavior formulation for interactive motion prediction and planning
https://github.com/opendrivelab/betop

autonomous-driving motion-planning motion-prediction

Last synced: 7 days ago
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[NeurIPS 2024] Behavioral Topology (BeTop), a multi-agent behavior formulation for interactive motion prediction and planning

• Host: GitHub
• URL: https://github.com/opendrivelab/betop
• Owner: OpenDriveLab
• License: apache-2.0
• Created: 2024-03-31T07:17:41.000Z (9 months ago)
• Default Branch: main
• Last Pushed: 2024-11-12T09:25:41.000Z (about 2 months ago)
• Last Synced: 2024-12-19T22:07:01.580Z (14 days ago)
• Topics: autonomous-driving, motion-planning, motion-prediction
• Language: Python
• Homepage: https://arxiv.org/abs/2409.18031
• Size: 662 KB
• Stars: 65
• Watchers: 5
• Forks: 4
• Open Issues: 0
• Metadata Files:
  • Readme: README.md
  • Funding: .github/FUNDING.yml
  • License: LICENSE

Awesome Lists containing this project

README

        
 BeTop: Reasoning Multi-Agent Behavioral Topology 

for Interactive Autonomous Driving 
 




 

[![arXiv](https://img.shields.io/badge/arXiv-2409.18031-479ee2.svg)](https://arxiv.org/abs/2409.18031)














> - [Haochen Liu](https://scholar.google.com/citations?user=iizqKUsAAAAJ&hl), [Li Chen](https://scholar.google.com/citations?user=ulZxvY0AAAAJ&hl=zh-CN), Yu Qiao, Chen Lv and Hongyang Li  

> - [arXiv paper](https://arxiv.org/abs/2409.18031) | Slides TODO  

> - If you have any questions, please feel free to contact: *Haochen Liu* ( [email protected] )

---

**[2024-11]** Scenario Token released for ```Test14-Inter```. [Link](https://github.com/OpenDriveLab/BeTop/releases/download/nuplan/test14-inter.yaml)

**[2024-11]** Prediction project released.

Full code and checkpoints release is coming soon. Please stay tuned.

## Overview

**BeTop**  leverages braid theory to model multi-agent future behaviors in autonomous driving;











The synergetic framework, *BeTopNet*, integrates topology reasoning with prediction and planning tasks for autonomous driving.











## Get Started

### Prediction

We provide the full prediction implementation of BeTopNet in Waymo Open Motion Dataset (WOMD).

**Features:**

- :white_check_mark: Full support for WOMD Prediction Challenges  

- :white_check_mark: Flexible Toolbox for prediction tasks  

- :white_check_mark: Pipeline for reproduced popular Baselines

**[Guideline](womd/README.md)**

   - [Data Preparation](/docs/womd/DataPrep_pred.md)

   - [Training & Evaluation](/docs//womd/TrainEval_pred.md)

   - [Testing & Submission](/docs//womd/Submission_pred.md)

## TODO List

- [x] Initial release

- [x] Prediction pipeline in WOMD

- [ ] Planning pipeline in nuPlan

## Citation

If you find the project helpful for your research, please consider citing our paper:

```bibtex

@inproceedings{liu2024betop,

 title={Reasoning Multi-Agent Behavioral Topology for Interactive Autonomous Driving}, 

 author={Haochen Liu and Li Chen and Yu Qiao and Chen Lv and Hongyang Li},

 booktitle={NeurIPS},

 year={2024}

}

```