Distracted Driving Detection through the Analysis of Real-time Driver, Vehicle, and Roadway Volatilities

Common examples of distracted driving are secondary activities, such as cell phone use, eating, talking with passengers, adjusting vehicle infotainment controls, and looking at roadside elements or advertisement boards. Distracted driving usually gives rise to driving instability which leads to increased crash risk and higher crash frequency. Early detection of driver distraction is critical to prevent traffic crashes by providing feedback and warning messages to drivers and the surrounding vehicles. This study harnesses real-time multidimensional data collected through sensors that examine the variations in driver biometrics, vehicle kinematics, and roadway surroundings in different driving scenarios conducted on a Multimodal Virtual Reality Simulator. The driving behaviors of the study participants were examined under various visual detection response tasks of increasing complexity. The study classifies driving behaviors as normal and distracted on a 5-level ordinal scale by estimating a Panel Ordered Logit Model, Random Forest, and Artificial Neural Network, using real-time volatilities in driver biometric signals, vehicle speed and acceleration, and roadway surroundings. The study results reveal that the driver gaze and the coefficients of variation in vehicle speed, driver eye movements, vehicular distances from the lane centreline, and the following vehicle significantly impact distracted driving. The study’s findings align with the principles of the safe systems approach by emphasizing the development of proactive safety measures in the form of feedback and warning the driver and surrounding vehicles of a potential distracted driving event, helping to foster safer user behavior and vehicles.