---
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** | [![Status](https://img.shields.io/website?up_message=online&down_message=offline&url=https%3A%2F%2FBaseerAI-baseer-server.hf.space)](https://BaseerAI-baseer-server.hf.space) |
| **Model** | [![Model](https://img.shields.io/badge/Model-Interfuser--Baseer--v1-blue)](https://huggingface.co/BaseerAI/Interfuser-Baseer-v1) |
| **Frameworks** | [![FastAPI](https://img.shields.io/badge/FastAPI-005571?style=flat&logo=fastapi)](https://fastapi.tiangolo.com/) [![PyTorch](https://img.shields.io/badge/PyTorch-%23EE4C2C.svg?style=flat&logo=PyTorch&logoColor=white)](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/BaseerAI/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/BaseerAI/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. The typical workflow is to start a session, run steps in a loop, and then end the session.

### 1. Start a New Session
This will initialize a new set of tracker and controller instances on the server.

**Request:**
```bash
curl -X POST "https://BaseerAI-baseer-server.hf.space/start_session"
```

**Example Response:**
```json
{
  "session_id": "a1b2c3d4-e5f6-7890-1234-567890abcdef"
}
```

### 2. Run a Simulation Step

Send the current camera view and vehicle measurements to be processed. The API will return control commands and a full analysis.

**Request:**
```bash
curl -X POST "https://BaseerAI-baseer-server.hf.space/run_step" \
  -H "Content-Type: application/json" \
  -d '{
    "session_id": "a1b2c3d4-e5f6-7890-1234-567890abcdef",
    "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]
    }
  }'
```

**Example Response:**
```json
{
  "control_commands": {
    "steer": 0.05,
    "throttle": 0.6,
    "brake": false
  },
  "scene_analysis": {
    "is_junction": 0.02,
    "traffic_light_state": 0.95,
    "stop_sign": 0.01
  },
  "predicted_waypoints": [
    [1.0, 0.05],
    [2.0, 0.06],
    [3.0, 0.07],
    [4.0, 0.07],
    [5.0, 0.08],
    [6.0, 0.08],
    [7.0, 0.09],
    [8.0, 0.09],
    [9.0, 0.10],
    [10.0, 0.10]
  ],
  "dashboard_b64": "a-very-long-base64-string-representing-the-dashboard-image...",
  "reason": "Red Light"
}
```

**Response Fields:**
- **`control_commands`**: The final commands to be applied to the vehicle.
- **`scene_analysis`**: Probabilities for different road hazards. A high `traffic_light_state` value (e.g., > 0.5) indicates a red light.
- **`predicted_waypoints`**: The model's intended path, relative to the vehicle.
- **`dashboard_b64`**: A Base64-encoded JPEG image of the full dashboard view, which can be directly displayed in a client application.
- **`reason`**: A human-readable string explaining the primary reason for the control action (e.g., "Following ID 12", "Red Light", "Cruising").

### 3. End the Session

This will clean up the session data from the server.

**Request:**
```bash
curl -X POST "https://BaseerAI-baseer-server.hf.space/end_session?session_id=a1b2c3d4-e5f6-7890-1234-567890abcdef"
```

**Example Response:**
```json
{
  "message": "Session a1b2c3d4-e5f6-7890-1234-567890abcdef ended."
}
```

---

## 📡 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 Altawil  
**License:** MIT