---
license: apache-2.0
library_name: transformers
pipeline_tag: image-text-to-text
language:
  - en
base_model: Qwen/Qwen3.5-2B
datasets:
  - zwc2003/DriveMA_Datasets
tags:
  - autonomous-driving
  - vision-language-action
  - trajectory-planning
  - multimodal
  - qwen3.5
  - "arxiv:2605.31271"
---

# DriveMA-2B

DriveMA-2B is the official 2B checkpoint accompanying
**[DriveMA: Driving Vision-Language-Action Models with Verifiable Meta-Actions](https://huggingface.co/papers/2605.31271)**.
It is fine-tuned from [Qwen3.5-2B](https://huggingface.co/Qwen/Qwen3.5-2B)
using the DriveMA three-stage pipeline: action-centric pretraining,
action-conditioned trajectory supervised fine-tuning, and turn-level
reinforcement learning.

DriveMA formulates driving planning as a two-turn generation process. The first
turn predicts a compact, interpretable meta-action from multi-view observations
and vehicle state. The second turn generates future waypoints conditioned on
that meta-action.

## Resources

- **Paper:** [Hugging Face Papers](https://huggingface.co/papers/2605.31271) · [arXiv:2605.31271](https://arxiv.org/abs/2605.31271)
- **Code:** [Tsinghua-MARS-Lab/DriveMA](https://github.com/Tsinghua-MARS-Lab/DriveMA)
- **Dataset annotations:** [zwc2003/DriveMA_Datasets](https://huggingface.co/datasets/zwc2003/DriveMA_Datasets)
- **Base model:** [Qwen/Qwen3.5-2B](https://huggingface.co/Qwen/Qwen3.5-2B)

## Loading

DriveMA-2B uses the same model architecture, processor, and standard loading
interface as Qwen3.5-2B:

```python
from transformers import AutoModelForMultimodalLM, AutoProcessor

model_id = "zwc2003/DriveMA-2B"

processor = AutoProcessor.from_pretrained(model_id)
model = AutoModelForMultimodalLM.from_pretrained(
    model_id,
    torch_dtype="auto",
    device_map="auto",
)
```

For general multimodal inference, follow the
[Qwen3.5-2B usage instructions](https://huggingface.co/Qwen/Qwen3.5-2B).
To reproduce the model's driving-planning behavior, start from the
[official DriveMA repository](https://github.com/Tsinghua-MARS-Lab/DriveMA)
and use its
[inference scripts and prompt templates](https://github.com/Tsinghua-MARS-Lab/DriveMA/tree/main/tools/infer_scripts),
which implement the expected multi-view inputs, vehicle-state fields, two-turn
interaction, and output format.

## Results

On the Waymo Open Dataset vision-based end-to-end planning benchmark, the paper
reports the following results for DriveMA-2B:

| RFS Overall ↑ | RFS Spotlight ↑ | ADE@5s ↓ | ADE@3s ↓ |
|---:|---:|---:|---:|
| 8.060 | 7.251 | 2.616 | 1.154 |

See the paper and code repository for the full evaluation protocol,
comparisons, and ablations.

## Intended Use and Limitations

DriveMA-2B is intended for research on vision-language-action modeling and
end-to-end autonomous-driving planning. The released dataset repository
contains annotations; users must obtain the corresponding source image/video
assets under their original licenses and update local paths as described in the
code repository.

This model is not validated for deployment in safety-critical systems and
should not be used to control a real vehicle without independent safety
validation, system-level safeguards, and compliance with applicable laws and
regulations.

## Citation

```bibtex
@article{zheng2026drivema,
  title={DriveMA: Driving Vision-Language-Action Models with Verifiable Meta-Actions},
  author={Zheng, Weicheng and Huang, Yixin and Sun, Qiao and Li, Derun and Zhao, Hang},
  journal={arXiv preprint arXiv:2605.31271},
  year={2026}
}
```