<div align="center"> <h3> MindDrive: A Vision-Language-Action Model for Autonomous Driving Utilizing Language as Action in Online Reinforcement Learning </h3>

Haoyu Fu<sup>1*</sup>, Diankun Zhang<sup>2*</sup>, Zongchuang Zhao<sup>1</sup>, <br>Jianfeng Cui<sup>2</sup>, Hongwei Xie<sup>2†</sup>, Bing Wang<sup>2</sup>, Guang Chen<sup>2</sup>, Dingkang Liang<sup>1†</sup>, Xiang Bai<sup>1</sup>

<sup>1</sup> Huazhong University of Science & Technology, <sup>2</sup> Xiaomi EV

(*) Equal contribution. (†) Project leader.

</div>

Abstract

Current Vision-Language-Action (VLA) paradigms in autonomous driving primarily rely on Imitation Learning (IL), which introduces inherent challenges such as distribution shift and causal confusion. Online Reinforcement Learning offers a promising pathway to address these issues through trial-and-error learning. However, applying online reinforcement learning to VLA models in autonomous driving is hindered by inefficient exploration in continuous action spaces. To overcome this limitation, we propose MindDrive, a VLA framework comprising a large language model (LLM) with two distinct sets of LoRA parameters. The one LLM serves as a Decision Expert for scenario reasoning and driving decision-making, while the other acts as an Action Expert that dynamically maps linguistic decisions into feasible trajectories. By feeding trajectory-level rewards back into the reasoning space, MindDrive enables trial-and-error learning over a finite set of discrete linguistic driving decisions, instead of operating directly in a continuous action space. This approach effectively balances optimal decision-making in complex scenarios, human-like driving behavior, and efficient exploration in online reinforcement learning. MindDrive achieves strong closed-loop performance on the challenging Bench2Drive benchmark, with a Driving Score (DS) of 78.04 and a Success Rate (SR) of 55.09%. To the best of our knowledge, this is the first work to demonstrate the effectiveness of online reinforcement learning for the VLA model in autonomous driving.

Overview

News

[2026/03/19] We are launching the MindDrive 3B model, which achieves 80.59 DS and 55.09 SR. Code and weights are now available.

[2026/02/08] Minddrive code and dataset are now released!

[2025/12/16] ArXiv paper release.

Currently Supported Features

[√] MindDrive Inference Framework
[√] Close-loop Evaluation
[√] MindDrive Checkpoint
[√] MindDrive Training Framework

Getting Started

git clone https://github.com/xiaomi-mlab/MindDrive.git
cd ./MindDrive
conda create -n MindDrive python=3.8 -y
conda activate MindDrive
pip install torch==2.4.1+cu118 torchvision==0.19.1+cu118 torchaudio==2.4.1 --index-url https://download.pytorch.org/whl/cu118
pip install -v -e .
pip install -r requirements.txt

Download and setup CARLA 0.9.15

mkdir /home/carla
cd /home/carla
wget https://carla-releases.s3.us-east-005.backblazeb2.com/Linux/CARLA_0.9.15.tar.gz
tar -xvf CARLA_0.9.15.tar.gz
cd Import && wget https://carla-releases.s3.us-east-005.backblazeb2.com/Linux/AdditionalMaps_0.9.15.tar.gz
cd .. && bash ImportAssets.sh
export CARLA_ROOT=/home/carla
echo "$CARLA_ROOT/PythonAPI/carla/dist/carla-0.9.15-py3.7-linux-x86_64.egg" >> YOUR_CONDA_PATH/envs/MindDrive/lib/python3.8/site-packages/carla.pth

Preparation

To help reproduce the results of Minddrive, we update the Chat-B2D dataset by incorporating meta-action VQA for each frame. The dataset is available at here.

We pretrain the 2D LLM weights and the vision encoder + projector weights for the Qwen2-0.5B and Qwen2.5-3B model, following the approach of Orion.

cd /path/to/Minddrive
mkdir ckpts

Train

Prepare data

Unzip the dataset:

unzip Chat-B2D-plus.zip -d data/

Imitation Learning

Following Orion’s approach, this project uses a three-stage training pipeline (stage1, stage2, stage3). In the imitation learning stage we build a one-to-one mapping between language and trajectories. If you are using the 3B model, please modify the corresponding configuration file.

./adzoo/minddrive/minddrive_dist_train.sh adzoo/minddrive/configs/minddrive_qwen2_05b_train_stage1.py $GPU
# or
./adzoo/minddrive/minddrive_dist_train.sh adzoo/minddrive/configs/minddrive_qwen2_05b_train_stage2(3).py $GPU

To save training time and GPU memory, we only train the LoRA of the action expert. After Imitation Learning, we copy the action expert’s weights into the decision expert and the value net so they share the trained representations.

python rl_projects/convert_checkpoint.py

Reinforcement Learning

Rollout (data collection)

If you are using the 3B model, remember to update the CONFIG in the file.

bash adzoo/minddrive/minddrive_run_collection_multi.sh

The rollout script collects interaction data. The dataset is automatically decoupled and output to $DECOUPLE_OUTPUT.

RL training (PPO)

If you are using the 3B model, remember to update the corresponding config file first.

bash adzoo/minddrive/minddrive_run_mutil_train_ppo.sh 8 adzoo/minddrive/configs/minddrive_rl_ppo_train.py <imitation_weights_path> $DECOUPLE_OUTPUT/dataset_index.pkl

Results and Checkpoints

Orion and other baselines

| Method | L2 (m) 2s | Driving Score | Success Rate(%) | Config | Download | Eval Json| | :---: | :---: | :---: | :---: | :---: | :---: | :---: | | UniAD-Tiny |0.80 | 40.73 | 13.18 | config | Hugging Face/Baidu Cloud| Json | | UniAD-Base |0.73 | 45.81 | 16.36 | config | Hugging Face/Baidu Cloud | Json | | VAD |0.91 | 42.35 | 15.00 | config | Hugging Face/Baidu Cloud | Json | | ORION-7B |0.68 | 77.74 | 54.62 | config | Hugging Face| Json | MindDrive<sub>0.5B</sub> |0.69 | 78.04 | 55.09 | config | Hugging Face | Json | MindDrive<sub>3B</sub> | 0.66 | 80.59 | 58.26 | config | Hugging Face | Json |

Data Usage Statement

This project uses the following external resources:

Data: We use the dataset provided in the Bench2Drive project (source: https://github.com/Thinklab-SJTU/Bench2Drive), which is licensed under the CC BY-NC-ND 4.0 license.

Citation

If this work is helpful for your research, please consider citing:

@article{fu2025minddrive,
  title={MindDrive: A Vision-Language-Action Model for Autonomous Driving via Online Reinforcement Learning},
  author={Haoyu Fu and Diankun Zhang and Zongchuang Zhao and Jianfeng Cui and Hongwei Xie and Bing Wang and Guang Chen and Dingkang Liang and Xiang Bai},
  journal={arXiv Preprint arXiv:2512.13636},  
  year={2025},
}

@inproceedings{fu2025orion,
  title={ORION: A Holistic End-to-End Autonomous Driving Framework by Vision-Language Instructed Action Generation},
  author={Haoyu Fu and Diankun Zhang and Zongchuang Zhao and Jianfeng Cui and Dingkang Liang and Chong Zhang and Dingyuan Zhang and Hongwei Xie and Bing Wang and Xiang Bai},
  booktitle={Proceedings of the IEEE/CVF International Conference on Computer Vision},
  year={2025}
}

MindDrive

Install / Use

README