| --- |
| license: mit |
| language: en |
| library_name: pytorch |
| tags: |
| - computer-vision |
| - autonomous-driving |
| - self-driving-car |
| - end-to-end |
| - transformer |
| - attention |
| - positional-encoding |
| - carla |
| - object-detection |
| - trajectory-prediction |
| datasets: |
| - PDM-Lite-CARLA |
| pipeline_tag: object-detection |
| --- |
| |
| # HDPE: A Foundational Perception Model with Hyper-Dimensional Positional Encoding |
|
|
| [](https://opensource.org/licenses/MIT) |
| [](https://pytorch.org/) |
| [](https://carla.org/) |
| [](https://huggingface.co/spaces/Adam-IT/Baseer_Server) |
|
|
| **📖 Research Paper (Coming Soon)** | **🚀 [Live Demo API (Powered by this Model)](https://huggingface.co/spaces/BaseerAI/Baseer_Server)** |
|
|
| --- |
|
|
| ## 📖 Overview: A New Foundation for Perception in Autonomous Driving |
|
|
| This repository contains the pre-trained weights for a novel autonomous driving perception model, the core of our **Interfuser-HDPE** system. This is **not a standard Interfuser model**; it incorporates fundamental innovations in its architecture and learning framework to achieve a more robust, accurate, and geometrically-aware understanding of driving scenes from camera-only inputs. |
|
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| The innovations baked into these weights make this model a powerful foundation for building complete self-driving systems. It is designed to output rich perception data (object detection grids and waypoints) that can be consumed by downstream modules like trackers and controllers. |
|
|
| --- |
|
|
| ## 💡 Key Innovations in This Model |
|
|
| The weights in this repository are the result of training a model with the following scientific contributions: |
|
|
| ### 1. Hyper-Dimensional Positional Encoding (HDPE) - (Core Contribution) |
| * **What it is:** We replace the standard Sinusoidal Positional Encoding with **HDPE**, a novel, first-principles approach inspired by the geometric properties of n-dimensional spaces. |
| * **Why it matters:** HDPE generates an interpretable spatial prior that biases the model's attention towards the center of the image (the road ahead). This leads to more stable and contextually-aware feature extraction, and has shown to improve performance significantly, especially in multi-camera fusion scenarios. |
|
|
| ### 2. Advanced Multi-Task Loss Framework |
| * **What it is:** This model was trained using a specialized combination of **Focal Loss** and **Enhanced-IoU (EIoU) Loss**. |
| * **Why it matters:** This framework is purpose-built to tackle the primary challenges in perception: **Focal Loss** addresses the severe class imbalance in object detection, while **EIoU Loss** ensures highly accurate bounding box regression by optimizing for geometric overlap. |
|
|
| ### 3. High-Resolution, Camera-Only Architecture |
| * **What it is:** This model is vision-based (**camera-only**) and uses a **ResNet-50** backbone with a smaller patch size (`patch_size=8`) for high-resolution analysis. |
| * **Why it matters:** It demonstrates that strong perception performance can be achieved without costly sensors like LiDAR, aligning with modern, cost-effective approaches to autonomous driving. |
|
|
| --- |
|
|
| ## 🏗️ Model Architecture vs. Baseline |
|
|
| | Component | Original Interfuser (Baseline) | **Interfuser-HDPE (This Model)** | |
| |:--------------------------|:-------------------------------|:----------------------------------| |
| | **Positional Encoding** | Sinusoidal PE | ✅ **Hyper-Dimensional PE (HDPE)** | |
| | **Perception Backbone** | ResNet-26, LiDAR | ✅ **Camera-Only, ResNet-50** | |
| | **Training Objective** | Standard BCE + L1 Loss | ✅ **Focal Loss + EIoU Loss** | |
| | **Model Outputs** | Waypoints, Traffic Grid, States| Same (Optimized for higher accuracy) | |
|
|
| --- |
|
|
| ## 🚀 How to Use These Weights |
|
|
| These weights are intended to be loaded into a model class that incorporates our architectural changes, primarily the `HyperDimensionalPositionalEncoding` module. |
|
|
| ```python |
| import torch |
| from huggingface_hub import hf_hub_download |
| # You need to provide the model class definition, let's call it InterfuserHDPE |
| from your_model_definition_file import InterfuserHDPE |
| |
| # Download the pre-trained model weights |
| model_path = hf_hub_download( |
| repo_id="BaseerAI/Interfuser-Baseer-v1", |
| filename="pytorch_model.bin" |
| ) |
| |
| # Instantiate your model architecture |
| # The config must match the architecture these weights were trained on |
| device = torch.device("cuda" if torch.cuda.is_available() else "cpu") |
| model = InterfuserHDPE(**model_config).to(device) |
| |
| # Load the state dictionary |
| state_dict = torch.load(model_path, map_location=device) |
| model.load_state_dict(state_dict) |
| model.eval() |
| |
| # Now the model is ready for inference |
| with torch.no_grad(): |
| # The model expects a dictionary of sensor data |
| # (e.g., {'rgb': camera_tensor, ...}) |
| perception_outputs = model(input_data) |
| ``` |
|
|
| ## 📊 Performance Highlights |
|
|
| When integrated into a full driving stack (like our **[Baseer Self-Driving API](https://huggingface.co/spaces/BaseerAI/Baseer_Server)**), this perception model is the foundation for: |
|
|
| - **Significantly Improved Detection Accuracy**: Achieves higher mAP on the PDM-Lite-CARLA dataset. |
| - **Superior Driving Score**: Leads to a higher overall Driving Score with fewer infractions compared to baseline models. |
| - **Proven Scalability**: Performance demonstrably improves when scaling from single-camera to multi-camera inputs, showcasing the robustness of the HDPE-based architecture. |
|
|
| *(Detailed metrics and ablation studies will be available in our upcoming research paper.)* |
|
|
| ## 🛠️ Integration with a Full System |
|
|
| This model provides the core perception outputs. To build a complete autonomous agent, you need to combine it with: |
|
|
| - **A Temporal Tracker**: To maintain object identity across frames. |
| - **A Decision-Making Controller**: To translate perception outputs into vehicle commands. |
|
|
| An example of such a complete system, including our custom-built **Hierarchical, Memory-Enhanced Controller**, can be found in our **[Live Demo API Space](https://huggingface.co/spaces/BaseerAI/Baseer_Server)**. |
|
|
| ## 📚 Citation |
|
|
| If you use the HDPE concept or this model in your research, please cite our upcoming paper. For now, you can cite this model repository: |
|
|
| ```bibtex |
| @misc{interfuser-hdpe-2024, |
| title={HDPE: Hyper-Dimensional Positional Encoding for End-to-End Self-Driving Systems}, |
| author={Altawil, Adam}, |
| year={2024}, |
| publisher={Hugging Face}, |
| howpublished={\url{https://huggingface.co/BaseerAI/Interfuser-Baseer-v1}} |
| } |
| ``` |
|
|
| ## 👨💻 Development |
|
|
| **Lead Researcher**: Adam Altawil |
| **Project Type**: Graduation Project - AI & Autonomous Driving |
| **Contact**: [Your Contact Information] |
|
|
| ## 📄 License |
|
|
| This project is licensed under the MIT License - see the [LICENSE](LICENSE) file for details. |
|
|
| ## 🤝 Contributing & Support |
|
|
| For questions, contributions, and support: |
| - **🚀 Try the Live Demo**: **[Baseer Server Space](https://huggingface.co/spaces/BaseerAI/Baseer_Server)** |
| - **📧 Contact**: [Your Contact Information] |
| - **🐛 Issues**: Create an issue in this repository |
|
|
| --- |
|
|
| <div align="center"> |
| <strong>🚗 Driving the Future with Hyper-Dimensional Intelligence 🚗</strong> |
| </div> |
