🚗 AI-Based Driver Behavior Analysis System

📚 Project Overview

The AI-Based Driver Behavior Analysis System aims to improve road safety by analyzing driving patterns and providing insights into driver behavior. This system leverages machine learning algorithms to detect risky driving habits, such as speeding or abrupt braking, and provides feedback to help drivers adopt safer driving practices. The solution can also be integrated with insurance companies to offer personalized insurance plans based on individual driving styles.

🌟 Features

• 🚦 Driving Pattern Analysis: Detects common driving behaviors like speeding, sudden acceleration, and harsh braking.

• ⚠️ Risk Assessment: Evaluates driver behavior and provides a safety score based on data analysis.

• 📊 Data Visualization: Visual representation of driving patterns to help understand areas of improvement.

• 🤖 Machine Learning Integration: Utilizes machine learning models to predict risky behaviors and offer real-time feedback.

💻Technologies Used

• Programming Language: 🐍Python
• Libraries and Frameworks:
  • 📊 Pandas for data manipulation
  • 🔢 NumPy for numerical operations
  • 🧠 scikit-learn for implementing machine learning algorithms
  • 📈 Matplotlib and Seaborn for data visualization
• Data Sources: Publicly available datasets on driver behavior or simulated driving data

📊 Data Source

The driving behavior data used in this project was obtained from the Driving Behavior Dataset by Yuksel, Asim Sinan; Atmaca, Şerafettin (2020), available at Mendeley Data.

Citation:

Yuksel, Asim Sinan; Atmaca, Şerafettin (2020), “Driving Behavior Dataset”, Mendeley Data, V2, doi: 10.17632/jj3tw8kj6h.2

📂 Project Structure

The project follows a well-structured layout to ensure modularity and ease of maintenance:

├── api                 # api code to interact with model
│   └── models          # deployment ready models (copied from models/models)
├── config              # project level configurations
│   └── environments    # environment configurations (ex. dotenv)
├── deployment          # deployment related files
│   └── docker          # files for building and managing docker images
│   └── pipelines       # ci/cd pipelines (if your CI/CD system allows custom directory)
│   └── scripts         # supporting scripts for building and deploying
├── docs                # global documentation
├── infrastructure      # code for managing the project's infrastructure
├── src                 # source code for model development
│   ├── data            # scripts for collecting and processing data
│   ├── datasets        # processed data (ex. medallion architecture)
│   │   ├── bronze      # raw, unprocessed data
│   │   ├── gold        # final processed data
│   │   └── silver      # intermediate processed data
│   ├── models          # deployment ready models
│   ├── notebooks       # jupyter notebooks
│   ├── scripts         # training and testing scripts
│   │   ├── tests       # scripts for running tests on models
│   │   └── training    # scripts for training models
│   └── utils           # utility scripts
├── ui                  # user interface for interacting with model api
├── .gitignore          # list of files and/or directories that are not stored in the repository
├── Makefile            # file to automate common development tasks
└── README.md           # repository documentation

Credit: The project structure was inspired by the article "How to Structure a Machine Learning Project for Optimal MLOps Efficiency" by Craftwork AI on Medium.

🚀 Installation

1. Clone the repository:

   git clone https://github.com/your-username/driver-behavior-analysis.git
   cd driver-behavior-analysis

2. Create a virtual environment:

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

3. Install the required dependencies:

pip install -r requirements.txt

🛠️ Usage

1. Data Preparation:

• Use the provided scripts to clean and preprocess the driving data.
• Load the dataset into the system for analysis.

2. Run the analysis:

python analyze_driver_behavior.py

3. Visualize the results:

• View the analysis results, including risk scores and driving pattern graphs.

📉 Data Visualization

The system provides interactive graphs and charts to represent driver behavior data. Visualizing data helps in understanding driving patterns and identifying areas for improvement.

🔮 Future Enhancements

• 📡 Real-time Data Integration: Connect the system to real-time vehicle data using OBD-II devices.
• 🧬 Advanced Machine Learning Models: Implement deep learning techniques for more accurate behavior predictions.
• 🤝 Integration with Insurance Providers: Collaborate with insurance companies to offer personalized insurance plans based on driver scores.

🤝 Contribution

Contributions to the project are welcome. If you have ideas or improvements, feel free to create an issue or submit a pull request.

📝 License

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

📬 Contact

For any questions or suggestions, please reach out to:

• 📧 Email: [email protected]
• 💼 LinkedIn: Raisun Lakra
• 🐱 GitHub: RaisunLakra