Autonomous Vehicle Control System

This project demonstrates the control of an autonomous vehicle using A* pathfinding, sensor data integration, and object detection with YOLO. The system is designed to operate within a simulated environment provided by AirSim.

Table of Contents

• Introduction
• Features
• Installation
• Usage
• Project Structure
• Dependencies

Introduction

The Autonomous Vehicle Control System leverages A* pathfinding for navigation, integrates various sensor data for obstacle detection, and employs YOLO for object detection. The project aims to simulate autonomous vehicle behavior in a controlled environment.

Features

• A Pathfinding Algorithm*: Efficient pathfinding from start to goal coordinates.
• Sensor Data Integration: Utilizes distance sensors to detect obstacles.
• Object Detection: Implements YOLOv5 for real-time object detection.
• Pure Pursuit Control Algorithm: Smooth path following for the vehicle.

Installation

To set up the project, follow these steps:

1. Clone the Repository

   git clone https://github.com/oaslananka/Airsim101_Yolov10.git
   cd autonomous-vehicle-control

2. Create a Virtual Environment

   python -m venv venv
   source venv/bin/activate  # On Windows use `venv\Scripts\activate`

3. Install Dependencies

   pip install -r requirements.txt

4. Install AirSim

   Follow the instructions to install AirSim.

Usage

1. Configure AirSim: Ensure AirSim is correctly set up and running.

2. Run the Main Script

   python -m core.main

3. Monitor Output: The vehicle will start navigating the environment based on the defined path.

Project Structure

autonomous-vehicle-control/
│
├── README.md
├── requirements.txt
├── setup.py
│
├── config/
│   ├── __init__.py
│   ├── coordinates.py
│   └── graph.py
│
├── core/
│   ├── __init__.py
│   ├── astar.py
│   ├── control.py
│   ├── main.py
│   └── sensors.py
│
├── utils/
│   ├── __init__.py
│   └── common.py
│
├── detection/
    ├── __init__.py
    └── object_detection.py

Dependencies

The project relies on several key libraries and frameworks:

• numpy: Fundamental package for scientific computing with Python.
• opencv-python: Library for computer vision.
• ultralytics: Implementation of the YOLO object detection model.
• airsim: Open-source simulator for autonomous vehicles from Microsoft AI & Research.

Install all dependencies using the provided `requirements.txt` file:

pip install -r requirements.txt

Acknowledgments

• AirSim by Microsoft for the simulation environment.
• Ultralytics YOLO for the object detection model.