Feature Extraction and AI Model-Based Detection of Driving Distraction Utilizing a Motion Platform

Distracted driving is a major factor in road accidents and fatalities, particularly due to the widespread use of mobile phones. This study aims to detect visual driving distractions using AI models, focusing on feature extraction and machine learning techniques to create an accurate detection method. Twenty-six participants took part in a simulated driving experiment using Euro Truck Simulator to create realistic driving scenarios. Participants completed two distinct scenarios: one involving a standard GPS-guided route and another while reading from a handheld smartphone to simulate distraction. Data was collected from both the simulator and an eye-tracking device, capturing critical metrics such as vehicle speed, steering angle, pedal usage, gaze direction, and head movement. The collected data was preprocessed by removing irrelevant timestamps, scaling values, and synchronizing the timestamps to a frequency of 20 Hz to ensure consistency. From this processed data, key features such as mean, standard deviation, minimum, maximum, and median values were extracted at 1 Hz intervals to provide a robust statistical summary of each signal. Several machine learning models were applied, with the Gaussian Process Classifier demonstrating the best performance, achieving an accuracy of 0.976 and an AUC of 0.996. These results highlight the potential of AI-driven systems to significantly enhance driving safety by accurately identifying distracted driving behaviors, paving the way for real-time driver monitoring solutions.