---
library_name: transformers
license: apache-2.0
language:
- en
base_model:
- Qwen/Qwen2.5-VL-3B-Instruct
tags:
- VLM
- LLM
- DriveFusion
- Vision
- MultiModal
pipeline_tag: image-text-to-text
---
# DriveFusionQA Model Card

<div align="center">
  <img src="drivefusion_logo.png" alt="DriveFusion Logo" width="300"/>
  <h1>DriveFusionQA</h1>
  <p><strong>An Autonomous Driving Vision-Language Model for Scenario Understanding & Decision Reasoning.</strong></p>

  [![Model License](https://img.shields.io/badge/License-Apache%202.0-green.svg)](https://opensource.org/licenses/Apache-2.0)
  [![Base Model](https://img.shields.io/badge/Base%20Model-Qwen2.5--VL-blue)](https://huggingface.co/Qwen/Qwen2.5-VL-7B-Instruct)
  [![Status](https://img.shields.io/badge/Status-Active-success.svg)]()
</div>

---

## 🚙 Model Description

**DriveFusionQA** is a specialized Vision-Language Model (VLM) fine-tuned to interpret complex driving scenes and explain vehicle decision-making. Built on the **Qwen2.5-VL** architecture, it bridges the gap between raw sensor data and human-understandable reasoning. 

Unlike general-purpose models, DriveFusionQA is specifically optimized to answer the "why" behind driving maneuvers, making it an essential tool for safety analysis, simulation, and interactive driving support.

## 🔗 GitHub Repository

Find the full implementation, training scripts, and preprocessing logic here:
* **Main Model Code:** [DriveFusion/drivefusion](https://github.com/DriveFusion/drivefusion)
* **Data Pipeline:** [DriveFusion/data-preprocessing](https://github.com/DriveFusion/data-preprocessing)

### Core Capabilities
* **Scenario Explanation:** Identifies traffic participants, road signs, and environmental hazards.
* **Decision Reasoning:** Justifies driving actions (e.g., "Braking due to a pedestrian entering the crosswalk").
* **Multi-Dataset Expertise:** Leverages a unified pipeline of world-class driving benchmarks.
* **Interactive Dialogue:** Supports multi-turn conversations regarding road safety and navigation.

---

## 📊 Model Performance

DriveFusionQA demonstrates significant improvements over the base model across all key driving-related language metrics. The substantial increase in **Lingo-Judge** scores reflects its superior ability to generate human-aligned driving reasoning.

| Model | Lingo-Judge | METEOR | CIDEr | BLEU |
| :--- | :---: | :---: | :---: | :---: |
| **DriveFusion QA** | **53.2** | **0.3327** | **0.1602** | **0.0853** |
| Qwen2.5-VL Base | 38.1 | 0.2577 | 0.1024 | 0.0259 |

---

## 📚 Training & Data

The model was trained using the [DriveFusion Data Preprocessing](https://github.com/DriveFusion/data-preprocessing) pipeline, which standardizes diverse autonomous driving datasets into a unified format.

**Key Datasets Included:**
* **LingoQA:** Action-focused scenery and decision components.
* **DriveGPT4 + BDD-X:** Human-like driving explanations and logic.
* **DriveLM:** Graph-based reasoning for autonomous driving.

---

## 🚀 Quick Start

Ensure you have the latest `transformers` library installed to support the Qwen2.5-VL architecture.

### Installation
```bash
pip install transformers accelerate pillow torch
```

### Inference Example
```python
from transformers import Qwen2_5_VLForConditionalGeneration, AutoProcessor
from PIL import Image
import torch

model_id = "DriveFusion/DriveFusionQA"
model = Qwen2_5_VLForConditionalGeneration.from_pretrained(
    model_id, torch_dtype="auto", device_map="auto"
)
processor = AutoProcessor.from_pretrained(model_id)

# Load driving scene
image = Image.open("driving_sample.jpg")
messages = [
    {
        "role": "user",
        "content": [
            {"type": "image", "image": image},
            {"type": "text", "text": "Describe the current driving scenario and any potential risks."},
        ],
    }
]

# Generate Response
text = processor.apply_chat_template(messages, tokenize=False, add_generation_prompt=True)
inputs = processor(text=[text], images=[image], return_tensors="pt").to("cuda")

output_ids = model.generate(**inputs, max_new_tokens=256)
response = processor.batch_decode(output_ids, skip_special_tokens=True)
print(response[0])
```

---

## 🛠 Intended Use
* **Safety Analysis:** Generating natural language reports for dashcam footage and near-miss events.
* **Training & Simulation:** Providing ground-truth explanations for AI driver training.
* **Interactive Assistants:** Assisting human operators or passengers with scene descriptions.

## ⚠️ Limitations
* **Hallucination:** Like all VLMs, it may occasionally misinterpret distant objects or complex social traffic cues.
* **Geographical Bias:** Performance may vary in regions or weather conditions not heavily represented in the training data.
* **Non-Control:** This model is for **reasoning and explanation**, not for direct vehicle control.