Steering Prediction for Quanser QCar(aiot_simulation_project)

This project implements a computer vision-based steering angle prediction system for the Quanser QCar platform. It utilizes deep learning techniques to analyze road images and predict appropriate steering angles, contributing to autonomous driving technology.

Features

• Real-time Processing: Analyzes road images and predicts steering angles in real-time.
• Deep Learning Model: Employs a sophisticated neural network for accurate angle estimation.
• Hardware Integration: Compatible with Quanser QCar and Logitech G923 steering wheel and pedals.
• Visualization: Provides intuitive visual output of prediction results.
• Multi-condition Support: Robust performance across various road conditions and environments.
• Mulit-control Qcar: User controls the cart via keyboard or Logitech g923 steering wheel.

Tech Stack

• Backend: Python
• Computer Vision: OpenCV
• Deep Learning: TensorFlow/PyTorch (specify which one you're using)
• Data Processing: NumPy
• Hardware Control: Quanser Real-Time Control Software, Logitech G HUB Software

Hardware

• Quanser QCar
• Logitech G923 Steering Wheel and Pedals
• (Add any other specific hardware components you're using)

Installation and Setup

To run this project locally, follow these steps:

1. Clone the repository:

   git clone https://github.com/calvinlee326/steering_angle.git

2. Navigate to the project directory:

   cd steering_angle

3. Install dependencies:

   pip install -r requirements.txt

4. Install Logitech G HUB Software for G923 wheel support.
5. Set up Quanser Real-Time Control Software according to Quanser's documentation.

Usage

1. Ensure all hardware (QCar, G923 wheel and pedals) is properly connected.
2. Run the main program:

   python main.py

3. Follow on-screen prompts to select input source and operation mode.

Project Demo

Here's a video demonstration and resources of the project:

• Demo Video Playlist https://youtube.com/playlist?list=PL1u-6FoD8-99JxeL8-x-_v6b3g7HRK4-9&si=Oj_umyPPfxTj9RvU
• Google Slide https://docs.google.com/presentation/d/1sH0aCrSh7xN83Yg5b_XWqtA_aXGHcvYNBqCC_lx7Wec/edit?usp=sharing
• Research Paper https://scholarworks.calstate.edu/concern/projects/br86bb49r

Contributing

Contributions are welcome! Please feel free to submit a Pull Request.

Contact

For any inquiries, please reach out via:

• GitHub: calvinlee326
• Email: calvinlee326@gmail.com
• LinkedIn: chunchenglee