Rule-Based Detection of Turns and Curves in Naturalistic Driving Using GPS and Gyroscope Data

This letter presents an interpretable, rule-based framework for detecting and classifying common driving maneuvers using naturalistic sensor data collected from older adult drivers. The method relies solely on GPS-derived heading and gyroscope-based angular velocity, avoiding traffic-sensitive variables such as speed and acceleration. A two-step approach was implemented: thresholding gyroscope signals to detect sharp maneuvers and analyzing monotonic trends in GPS heading to capture gradual maneuvers. Each detected maneuver was then classified into distinct categoriesloops, 90 turns, and curves (tightwide smoothsharp)using features such as heading change, peak angular velocity, and spatial extent. Evaluation on over 500 annotated events showed a classification accuracy of 98.6, with high performance across most maneuver types. The framework is sensor-efficient, robust to driving variability, and well-suited for real-world applications in driver behavior monitoring and safety assessment.