Tracking Driving Stressors through Multimodal Physiological Monitoring
Understanding and mitigating driving stress is important for improving road safety and driver well-being. Reliable estimation, however, requires distinguishing biobehavioral responses to individual stressors from gradual physiological and contextual changes. We collected physiological data and vehicle telemetry from 31 participants across 44 simulated-driving sessions containing controlled stressor events. Under cross-validation, a multimodal classifier achieved an AUROC of 0.768 when distinguishing the stressor phase from an earlier baseline, reflecting both stressor effects and temporal drift. Controlling for drift retained an AUROC of 0.661, but revealed stronger responses to sustained than brief stressors, and shifted feature attribution toward phasic cardiac and electrodermal markers. We further quantified the interaction between model-estimated physiological stress and observable changes in vehicle control through simulation telemetry. Our findings show that stressor-aware modeling can identify physiologically grounded responses that correspond to meaningful changes in driving behavior.