DriveFusion
/

DriveFusion-V0.2

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 ---
+# DriveFusion-V0.2 Model Card
+<div align="center">
+  <img src="https://raw.githubusercontent.com/DriveFusion/data-preprocessing/main/assets/drivefusion_logo.png" alt="DriveFusion Logo" width="300"/>
+  <h1>DriveFusion-V0.2</h1>
+  <p><strong>A Multimodal Autonomous Driving Model: Vision + Language + GPS + Speed.</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 Overview
+**DriveFusion-V0.2** is a state-of-the-art multimodal model designed for autonomous vehicle applications. Unlike standard Vision-Language models, V0.2 integrates **telemetry data** (GPS and Speed) directly into the transformer architecture to perform dual tasks:
+1. **Natural Language Reasoning**: Describing scenes and explaining driving decisions.
+2. **Trajectory & Speed Prediction**: Outputting coordinates for future waypoints and target velocity profiles.
+Built on the **Qwen2.5-VL** foundation, DriveFusion-V0.2 adds specialized MLP heads to fuse physical context with visual features, enabling a comprehensive "world model" for driving.
+### Core Features
+- **Vision Processing**: Handles images and videos via a 32-layer Vision Transformer.
+- **Context Fusion**: Custom `SpeedMLP` and `GPSTargetPointsMLP` integrate vehicle telemetry.
+- **Predictive Heads**: Generates **20 trajectory waypoints** and **10 target speed values**.
+- **Reasoning**: Full natural language generation for "Chain of Thought" driving explanations.
+---
+## 🏗 Architecture
+DriveFusion-V0.2 extends the Qwen2.5-VL architecture with a modular "Driving Intelligence" layer.
+### Technical Specifications
+- **Text Encoder**: Qwen2.5-VL (36 Transformer layers).
+- **Vision Encoder**: 32-layer ViT with configurable patch sizes.
+- **Driving MLPs**:
+  - `TrajectoryMLP`: Generates batch × 20 × 2 coordinates.
+  - `TargetSpeedMLP`: Generates batch × 10 × 1 velocity values.
+- **Context Window**: 128k tokens.
+---
+## 🚀 Quick Start
+Using DriveFusion-V0.2 requires the custom `DriveFusionProcessor` to handle the fusion of text, images, and telemetry.
+### Installation
+```bash
+pip install transformers accelerate qwen-vl-utils torch
+```
+### Inference Example
+```python
+import torch
+from drivefusion import DriveFusionForConditionalGeneration, DriveFusionProcessor
+# Load Model
+model_id = "DriveFusion/DriveFusion-V0.2"
+model = DriveFusionForConditionalGeneration.from_pretrained(model_id, torch_dtype=torch.float16).to("cuda")
+processor = DriveFusionProcessor.from_pretrained(model_id)
+# Define Input: Image + Prompt + Telemetry
+gps_context = [[40.7128, -74.0060], [40.7130, -74.0058]] # Lat/Lon history
+speed_context = [[30.5]] # Current speed in m/s
+message = [{
+    "role": "user",
+    "content": [
+        {"type": "image", "image": "highway_scene.jpg"},
+        {"type": "text", "text": "Analyze the scene and predict the next trajectory based on our current speed."}
+    ]
+}]
+# Generate
+inputs = processor(text=message, images="highway_scene.jpg", gps=gps_context, speed=speed_context, return_tensors="pt").to("cuda")
+output = model.generate(**inputs, max_new_tokens=512)
+# Results
+print("Reasoning:", output["text"])
+print("Predicted Trajectory (20 pts):", output["trajectory"])
+print("Target Speeds:", output["target_speeds"])
+```
+---
+## 🛠 Intended Use
+- **End-to-End Autonomous Driving**: Acting as a primary planner or a redundant safety checker.
+- **Explainable AI (XAI)**: Providing human-readable justifications for automated maneuvers.
+- **Sim-to-Real Transfer**: Using the model as a sophisticated "expert" driver in simulated environments.
+## ⚠️ Safety & Limitations
+- **Non-Real-Time Hardware**: This model is optimized for high-accuracy reasoning and may require quantization for low-latency onboard use.
+- **Physical Limits**: While the model predicts trajectories, it does not account for vehicle dynamics (e.g., tire friction) and should be used with a downstream controller.
+---
+## 📜 Citation
+If this model assists your research, please cite the DriveFusion graduation project:
+```bibtex
+@article{drivefusion2026v02,
+  title={DriveFusion-V0.2: Multimodal Trajectory Prediction and Reasoning},
+  author={DriveFusion Team},
+  year={2026},
+  publisher={GitHub},
+  url={https://github.com/DriveFusion/drivefusion}
+}
+```