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zwc2003
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Model Details
Model Provider
zwc2003
Model Tree
Qwen/Qwen3.5-2B
Input Modalities
Output Modalities
Supported Functionality
DriveMA-2B uses the same model architecture, processor, and standard loading interface as Qwen3.5-2B:
python
from transformers import AutoModelForMultimodalLM, AutoProcessormodel_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. To reproduce the model's driving-planning behavior, start from the official DriveMA repository and use its inference scripts and prompt templates, which implement the expected multi-view inputs, vehicle-state fields, two-turn interaction, and output format.
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.
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.
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}}