---
license: mit
language:
- en
tags:
- autonomous-driving
- vision-language-model
- computer-vision
- regression
base_model: Qwen/Qwen3-VL-2B-Instruct
datasets:
- jHaselberger/SADC-Situation-Awareness-for-Driver-Centric-Driving-Style-Adaptation
---

# 🚗 Autopilot-Qwen3-VL

**Autopilot-Qwen3-VL** is an end-to-end autonomous driving model built on top of the powerful `Qwen3-VL-2B-Instruct` Vision-Language Model. It takes a single road/dashcam image as input and directly predicts the vehicle's continuous control parameters: **target speed (km/h)** and **steering torque (N)**.

## 🎥 Simulation Demo

![Simulation Demo](https://huggingface.co/Aleton/Autopilot-qwen3-vl/resolve/main/demo.gif)

---

## 🧠 Model Details

The model utilizes a custom regression head on top of the frozen Qwen3-VL base, trained using Parameter-Efficient Fine-Tuning (PEFT/LoRA) for optimal performance and resource efficiency.

- **Base Model:** `Qwen/Qwen3-VL-2B-Instruct`
- **Total Parameters:** ~2.13B (2,132,320,770)
- **Trainable Parameters:** ~4.78M (4,788,738 / 0.225%)
- **Architecture Type:** Vision-Language Model + Dual Regression Head

![Architecture Diagram](architecture.jpg)

## 📊 Dataset & Output Format

Trained on the [SADC Situation Awareness Dataset](https://huggingface.co/datasets/jHaselberger/SADC-Situation-Awareness-for-Driver-Centric-Driving-Style-Adaptation). 

### ⚠️ Important Note on Steering Values
Based on the dataset's coordinate system (standard automotive physics):
- **Negative values (`-`)** = Steering **RIGHT**
- **Positive values (`+`)** = Steering **LEFT**

---

## 🚀 Usage

To run inference, you need the custom `autopilot_inference.py` script provided in this repository. 

### 1. Download the inference script
```python
from huggingface_hub import hf_hub_download

hf_hub_download(
    repo_id="Aleton/Autopilot-qwen3-vl", 
    filename="autopilot_inference.py", 
    local_dir="."
)
```

### 2. Run Inference
```python
from autopilot_inference import AutopilotInference
from PIL import Image

# 1. Load the model (downloads weights automatically)
autopilot = AutopilotInference.from_pretrained("Aleton/Autopilot-qwen3-vl")

# 2. Load a dashcam image
image = Image.open("road.jpg")

# 3. Get predictions
result = autopilot.predict(image)

print(f"Target Speed: {result['speed_kmh']:.1f} km/h")
print(f"Steering Torque: {result['steering_N']:.3f} N")
```

## ⚠️ Disclaimer
This model is built for educational and research purposes only. It is not designed, tested, or certified for use in real-world autonomous vehicles. Never rely on this model to control a real car.