Correlation Analysis between Vehicle Control Inputs and Physiological Signals for Driver State Monitoring

This paper investigates the correlation between vehicle control inputs and driver physiological states to enhance non-invasive monitoring in intelligent automotive environments. While traditional physiological sensors (ECG, EDA) provide high-fidelity data regarding stress and cognitive load, their physical discomfort often limits practical application in naturalistic driving. To address this, we propose the use of vehicle mechanical signals - steering wheel angle, throttle, and brake pedal demand - as virtual sensors for driver well-being. Using a high-fidelity driving simulator with a moving platform, we collected synchronized physiological and mechanical data from four subjects driving on a simulated circuit. Our analysis employs Spearman correlation to identify relationships between heart rate (HR), heart rate variability (HRV) metrics, and electrodermal activity (EDA) against driving maneuvers. Experimental results demonstrate a significant and consistent correlation between throttle pedal demand and mean heart rate (85%), suggesting that longitudinal control patterns could be good indicators for driver engagement and physiological arousal. These findings provide a foundation for developing unobtrusive ADAS that can infer driver mental state directly from vehicle bus data.