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| title: Baseer Self-Driving API | |
| emoji: 🚗 | |
| colorFrom: blue | |
| colorTo: red | |
| sdk: docker | |
| app_port: 7860 | |
| pinned: true | |
| license: mit | |
| short_description: A RESTful API for an InterFuser-based self-driving model. | |
| tags: | |
| - computer-vision | |
| - autonomous-driving | |
| - deep-learning | |
| - fastapi | |
| - pytorch | |
| - interfuser | |
| - graduation-project | |
| - carla | |
| - self-driving | |
| # 🚗 Baseer Self-Driving API | |
| | Service | Status | | |
| |---|---| | |
| | **API Status** | [](https://adam-it-baseer-server.hf.space) | | |
| | **Model** | [](https://huggingface.co/Adam-IT/Interfuser-Baseer-v1) | | |
| | **Frameworks** | [](https://fastapi.tiangolo.com/) [](https://pytorch.org/) | | |
| ## 📋 Project Description | |
| **Baseer** is an advanced self-driving system that provides a robust, real-time API for autonomous vehicle control. This Space hosts the FastAPI server that acts as an interface to the fine-tuned **[Interfuser-Baseer-v1](https://huggingface.co/Adam-IT/Interfuser-Baseer-v1)** model. | |
| The system is designed to take a live camera feed and vehicle measurements, process them through the deep learning model, and return actionable control commands and a comprehensive scene analysis. | |
| --- | |
| ## 🏗️ Architecture | |
| This project follows a decoupled client-server architecture, where the model and the application are managed separately for better modularity and scalability. | |
| ``` | |
| +-----------+ +------------------------+ +--------------------------+ | |
| | | | | | | | |
| | Client | -> | Baseer API (Space) | -> | Interfuser Model (Hub) | | |
| |(e.g.CARLA)| | (FastAPI Server) | | (Private/Gated Weights) | | |
| | | | | | | | |
| +-----------+ +------------------------+ +--------------------------+ | |
| HTTP Loads Model Model Repository | |
| Request | |
| ``` | |
| ## ✨ Key Features | |
| ### 🧠 **Advanced Perception Engine** | |
| - **Powered by:** The [Interfuser-Baseer-v1](https://huggingface.co/Adam-IT/Interfuser-Baseer-v1) model. | |
| - **Focus:** High-accuracy traffic object detection and safe waypoint prediction. | |
| - **Scene Analysis:** Real-time assessment of junctions, traffic lights, and stop signs. | |
| ### ⚡ **High-Performance API** | |
| - **Framework:** Built with **FastAPI** for high throughput and low latency. | |
| - **Stateful Sessions:** Manages multiple, independent driving sessions, each with its own tracker and controller state. | |
| - **RESTful Interface:** Intuitive and easy-to-use API design. | |
| ### 📊 **Comprehensive Outputs** | |
| - **Control Commands:** `steer`, `throttle`, `brake`. | |
| - **Scene Analysis:** Probabilities for junctions, traffic lights, and stop signs. | |
| - **Predicted Waypoints:** The model's intended path for the next 10 steps. | |
| - **Visual Dashboard:** A generated image that provides a complete, human-readable overview of the current state. | |
| --- | |
| ## 🚀 How to Use | |
| Interact with the API by making HTTP requests to its endpoints. | |
| ### 1. Start a New Session | |
| This will initialize a new set of tracker and controller instances on the server. | |
| ```bash | |
| curl -X POST "https://adam-it-baseer-server.hf.space/start_session" | |
| ``` | |
| Response: `{"session_id": "your-new-session-id"}` | |
| ### 2. Run a Simulation Step | |
| Send the current camera view and vehicle measurements to be processed. | |
| ```bash | |
| curl -X POST "https://adam-it-baseer-server.hf.space/run_step" \ | |
| -H "Content-Type: application/json" \ | |
| -d '{ | |
| "session_id": "your-new-session-id", | |
| "image_b64": "your-base64-encoded-bgr-image-string", | |
| "measurements": { | |
| "pos_global": [105.0, -20.0], | |
| "theta": 1.57, | |
| "speed": 5.5, | |
| "target_point": [10.0, 0.0] | |
| } | |
| }' | |
| ``` | |
| ### 3. End the Session | |
| This will clean up the session data from the server. | |
| ```bash | |
| curl -X POST "https://adam-it-baseer-server.hf.space/end_session?session_id=your-new-session-id" | |
| ``` | |
| --- | |
| ## 📡 API Endpoints | |
| | Endpoint | Method | Description | | |
| |---|---|---| | |
| | `/` | GET | Landing page with API status. | | |
| | `/docs` | GET | Interactive API documentation (Swagger UI). | | |
| | `/start_session` | POST | Initializes a new driving session. | | |
| | `/run_step` | POST | Processes a single frame and returns control commands. | | |
| | `/end_session` | POST | Terminates a specific session. | | |
| | `/sessions` | GET | Lists all currently active sessions. | | |
| --- | |
| ## 🎯 Intended Use Cases & Limitations | |
| ### ✅ Optimal Use Cases | |
| - Simulating driving in CARLA environments. | |
| - Research in end-to-end autonomous driving. | |
| - Testing perception and control modules in a closed-loop system. | |
| - Real-time object detection and trajectory planning. | |
| ### ⚠️ Limitations | |
| - **Simulation-Only:** Trained exclusively on CARLA data. Not suitable for real-world driving. | |
| - **Vision-Based:** Relies on a single front-facing camera and has inherent blind spots. | |
| - **No LiDAR:** Lacks the robustness of sensor fusion in adverse conditions. | |
| --- | |
| ## 🛠️ Development | |
| This project is part of a graduation thesis in Artificial Intelligence. | |
| - **Deep Learning:** PyTorch | |
| - **API Server:** FastAPI | |
| - **Image Processing:** OpenCV | |
| - **Scientific Computing:** NumPy | |
| ## 📞 Contact | |
| For inquiries or support, please use the **Community** tab in this Space or open an issue in the project's GitHub repository (if available). | |
| --- | |
| **Developed by:** Adam-IT | |
| **License:** MIT |