Autopilot Qwen3 Vl

--- 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 pipeline_tag: image-to-text --- # 🚗 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  --- ## 🧠 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  ## 📊 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...

Source context: 20 downloads · 1 likes · Pipeline image-to-text · Repo Aleton/Autowheel-qwen3-vl

Source context: 16 downloads · 0 likes · Pipeline image-to-text · Repo Aleton/Autopilot-qwen3-vl