Analyzing Driver Behavior Through Artificial Intelligence

Abstract

Analyzing driver behavior using artificial intelligence (AI) and machine learning (ML) enables proactive road safety measures by detecting patterns such as aggressive or distracted driving. In this work, we develop a driver behavior analysis framework leveraging smartphone and vehicle sensor data. The proposed system architecture integrates accelerometer, gyroscope, GPS, and CAN bus inputs into a data-processing pipeline[1][2]. Machine learning models (Random Forest, Support Vector Machine, Neural Networks, etc.) are trained to classify driving style (e.g. normal, aggressive, risky/drowsy) with high accuracy. Experimental evaluation shows ensemble methods like Gradient Boosting or Random Forest often achieve over 90% accuracy, while deep models (LSTM/GRU) effectively capture temporal driving patterns[3][4]. These results demonstrate that AI-driven analysis of inertial and vehicular data can accurately recognize and predict driver behavior, providing a foundation for advanced driver assistance and insurance telematics