HDPE: A Foundational Perception Model with Hyper-Dimensional Positional Encoding

📖 Research Paper (Coming Soon) | 🚀 Live Demo API (Powered by this Model)

📖 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.

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), 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.

📚 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:

@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 file for details.

🤝 Contributing & Support

For questions, contributions, and support:

🚀 Try the Live Demo: Baseer Server Space
📧 Contact: [Your Contact Information]
🐛 Issues: Create an issue in this repository

🚗 Driving the Future with Hyper-Dimensional Intelligence 🚗

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