--- 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 [![License: MIT](https://img.shields.io/badge/License-MIT-yellow.svg)](https://opensource.org/licenses/MIT) [![PyTorch](https://img.shields.io/badge/PyTorch-EE4C2C?style=flat&logo=pytorch&logoColor=white)](https://pytorch.org/) [![CARLA](https://img.shields.io/badge/CARLA-Simulator-blue)](https://carla.org/) [![Demo](https://img.shields.io/badge/🚀-Live%20Demo-brightgreen)](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. 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 ---
🚗 Driving the Future with Hyper-Dimensional Intelligence 🚗