BeTop

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<h1 align="center"> BeTop: Reasoning Multi-Agent Behavioral Topology for Interactive Autonomous Driving </h1> <div align="center">

</div> <div id="top" align="center"> <p align="center"> <img src="assets/betop_teaser.png" width="1000px" > </p> </div>

• Haochen Liu, Li Chen, Yu Qiao, Chen Lv and Hongyang Li
• Paper | Poster | Challenge Report
• If you have any questions, please feel free to contact: Haochen Liu ( haochen002@e.ntu.edu.sg )

<!-- > 📜 Preprint: <a href="https://arxiv.org/abs/2409.09016"><img src="https://img.shields.io/badge/arXiv-Paper-<color>"></a> -->

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[2025-06] The ensembled version of BeTop BeTop-ens has received 3rd place of 2025 WOMD Interaction Prediction Challenge. Report

[2024-11] Scenario Token released for Test14-Inter. Link

[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;

<div id="top" align="center"> <p align="center"> <img src="assets/betop.png" width="1000px" > </p> </div>

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

<div id="top" align="center"> <p align="center"> <img src="assets/betopnet.png" width="1000px" > </p> </div>

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

• Data Preparation
• Training & Evaluation
• Testing & Submission

TODO List

• [x] Initial release
• [x] Prediction pipeline in WOMD
• [x] Planning pipeline in nuPlan

Citation

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

@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}
}