autonomous-vehicle-simulator 1.0.1

Advanced autonomous vehicle simulation with PyQt5 GUI, AI path planning, and real-time physics

Advanced Autonomous Vehicle Simulation

A sophisticated autonomous vehicle simulator built with PyQt5 featuring advanced sensor systems, 3D visualization, and real-time simulation capabilities.

Features

Core Simulation

• 3D Visualization Engine: Real-time 3D environment with PyOpenGL/VTK integration
• Advanced Sensor Systems: LiDAR, cameras (RGB/depth/thermal/stereo), radar, ultrasonic, IMU/GPS
• Vehicle Physics: Realistic vehicle dynamics with configurable parameters
• Multi-vehicle Support: Multiple autonomous vehicles with V2V communication

Sensor Systems

• LiDAR: Configurable range, resolution, and point cloud visualization
• Cameras: Multiple camera types with real-time image processing
• Radar: Range-doppler visualization and target tracking
• Ultrasonic: Close-proximity detection
• IMU/GPS: Noise modeling and sensor fusion

Advanced Features

• Path Planning: A*, RRT, PRM algorithms with visualization
• Autonomous Algorithms: Waypoint following, lane keeping, obstacle avoidance
• Traffic Scenarios: Generation and playback system
• Data Recording: Sensor data recording and replay functionality
• Performance Metrics: Real-time dashboard with graphs

GUI Components

• Modern Dark Theme: Professional interface with customizable layouts
• Central 3D Viewport: Camera controls (orbit, pan, zoom, first-person)
• Sensor Panels: Live feeds from cameras and processed data
• Control Panel: Simulation parameters and vehicle settings
• Timeline Scrubber: Scenario playback and analysis
• Real-time Plots: Vehicle telemetry and sensor data visualization

Installation

1. Clone the repository:

git clone <repository-url>
cd robotics-simulation

2. Create a virtual environment:

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

3. Install dependencies:

pip install -r requirements.txt

4. Run the application:

python main.py

Project Structure

robotics-simulation/
├── main.py                 # Main application entry point
├── requirements.txt        # Python dependencies
├── README.md              # This file
├── config/                # Configuration files
│   ├── settings.yaml      # Application settings
│   └── scenarios/         # Pre-built scenarios
├── src/                   # Source code
│   ├── core/             # Core application components
│   ├── gui/              # GUI components and widgets
│   ├── simulation/       # Simulation engine
│   ├── sensors/          # Sensor models and processing
│   ├── visualization/    # 3D visualization engine
│   ├── algorithms/       # Path planning and AI algorithms
│   └── utils/            # Utility functions and helpers
├── assets/               # Resources (icons, models, textures)
├── data/                 # Data storage and exports
└── tests/                # Unit tests

Usage

Basic Operation

1. Launch the application
2. Load a scenario or create a new one
3. Configure vehicle and sensor parameters
4. Start the simulation
5. Monitor sensor data and vehicle performance

Advanced Features

• Custom Scenarios: Create and save custom simulation scenarios
• Sensor Configuration: Adjust sensor parameters in real-time
• Data Export: Export sensor data in standard formats (PCD, rosbag-like)
• Plugin System: Extend functionality with custom plugins

Development

Architecture

The application follows a modular architecture with clear separation of concerns:

• Core: Application lifecycle and main coordination
• GUI: User interface components and event handling
• Simulation: Physics engine and world simulation
• Sensors: Sensor models and data processing
• Visualization: 3D rendering and graphics
• Algorithms: Path planning and autonomous driving algorithms

Contributing

1. Fork the repository
2. Create a feature branch
3. Make your changes
4. Add tests for new functionality
5. Submit a pull request

License

This project is licensed under the MIT License - see the LICENSE file for details.

Acknowledgments

• PyQt5 for the GUI framework
• PyOpenGL/VTK for 3D visualization
• NumPy/SciPy for scientific computing
• OpenCV for computer vision algorithms