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

Abstract

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 categories—loops, 90° turns, and curves (tight/wide × smooth/sharp)—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.