WAM-Flow: Parallel Coarse-to-Fine Motion Planning via Discrete Flow Matching for Autonomous Driving

Yifang Xu^1*  Jiahao Cui^1*  Feipeng Cai^2*  Zhihao Zhu¹  Hanlin Shang¹  Shan Luan¹ 

Mingwang Xu¹  Neng Zhang²  Yaoyi Li²  Jia Cai²  Siyu Zhu¹ 

¹Fudan University  ²Yinwang Intelligent Technology Co., Ltd 

📰 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.