This thesis presents the design and implementation of a real-time system for detecting dangerous driving behavior using a model-based approach grounded in sensor data analysis. Unlike conventional systems that rely on GPS or georeferenced inputs, the proposed solution uses only inertial data, specifically, acceleration and gyroscopic measurements collected from a smartphone’s onboard IMU. This approach enables a low-cost, portable, and infrastructure-independent method for driving behavior evaluation. The methodology is based on a scientifically validated model for driving style classification, which was critically assessed and adapted. The initial phase involved validation within a controlled, virtual environment using Euro Truck Simulator 2, where acceleration data was gathered via a telemetry interface.