WAM-Flow: Parallel Coarse-to-Fine Motion Planning via Discrete Flow Matching for Autonomous Driving
📰 News
2026/02/01: 🎉🎉🎉 Release the pretrained models on Huggingface.2025/12/06: 🎉🎉🎉 Paper submitted on Arxiv.
📅️ Roadmap
| Status | Milestone | ETA |
|---|---|---|
| ✅ | Release the SFT and inference code | 2025.12.19 |
| ✅ | Pretrained models on Huggingface | 2026.02.01 |
| 🚀 | Release the evaluation code | TBD |
| 🚀 | Release the RL code | TBD |
| 🚀 | Release the pre-processed training data | TBD |
📸 Showcase
🏆 Qualitative Results on NAVSIM
NAVSIM-v1 benchmark results
NAVSIM-v2 benchmark results
🔧️ Framework
Our method takes as input a front-view image, a natural-language navigation command with a system prompt, and the ego-vehicle states, and outputs an 8-waypoint future trajectory spanning 4 seconds through parallel denoising. The model is first trained via supervised fine-tuning to learn accurate trajectory prediction. We then apply simulatorguided GRPO to further optimize closed-loop behavior. The GRPO reward function integrates safety constraints (collision avoidance, drivable-area compliance) with performance objectives (ego-progress, time-to-collision, comfort).
Quick Start
Installation
Clone the repo:
git clone https://github.com/fudan-generative-vision/WAM-Flow.git
cd WAM-Flow
Install dependencies:
conda create --name wam-flow python=3.10
conda activate wam-flow
pip install -r requirements.txt
Model Download
Download models using huggingface-cli:
pip install "huggingface_hub[cli]"
huggingface-cli download fudan-generative-ai/WAM-Flow --local-dir ./pretrained_model/wam-flow
huggingface-cli download LucasJinWang/FUDOKI --local-dir ./pretrained_model/fudoki
Inference
sh script/infer.sh
Training
sh script/sft_debug.sh
📝 Citation
If you find our work useful for your research, please consider citing the paper:
@article{xu2025wam,
title={WAM-Flow: Parallel Coarse-to-Fine Motion Planning via Discrete Flow Matching for Autonomous Driving},
author={Xu, Yifang and Cui, Jiahao and Cai, Feipeng and Zhu, Zhihao and Shang, Hanlin and Luan, Shan and Xu, Mingwang and Zhang, Neng and Li, Yaoyi and Cai, Jia and others},
journal={arXiv preprint arXiv:2512.06112},
year={2025}
}
⚠️ Social Risks and Mitigations
The integration of Vision-Language-Action models into autonomous driving introduces ethical challenges, particularly regarding the opacity of neural decision-making and its impact on road safety. To mitigate these risks, it is imperative to implement explainable AI frameworks and robust safe protocols that ensure predictable vehicle behavior in long-tailed scenarios. Furthermore, addressing concerns over data privacy and public surveillance requires transparent data governance and rigorous de-identification practices. By prioritizing safety-critical alignment and ethical compliance, this research promotes the responsible development and deployment of VLA-based autonomous systems.
🤗 Acknowledgements
We gratefully acknowledge the contributors to the Recogdrive, Janus, FUDOKI and flow_matching repositories, whose commitment to open source has provided us with their excellent codebases and pretrained models.