Real-Time Explainable AI for Driver Drowsiness Detection: Integrating SHAP-Based Visualizations and Actionable Counterfactuals

Driver drowsiness is implicated in up to 30% of traffic accidents worldwide, and timely alerts can save lives.However, current detection systems often trigger false alarms during normal behavior—such as rapid glances or conversational gestures—and offer no explanation for their decisions, undermining driver trust.We propose a novel, realtime explainable AI framework thatintegratesmultimodalinputs(eye-tracking,headpose,andsteeringmetrics)tocompute a continuous drowsiness confidence score.Our system uses SHAP (SHapley Additive exPlana- tions) to generate live visualizations that highlight feature contributions for each alarm.Addi- tionally, acounterfactualreasoning moduleprovidesactionablefeedbackbysuggestingminimal behavioral adjustments—such as reducing blink frequency or correcting head tilt—to prevent unnecessary alerts.Evaluated on two public benchmarks, our approach reduces false positives by 25% and increases driver trust ratings by 35% compared to state-of-the-art deep learning baselines. This work bridges the gap between high-accuracy detection and user interpretability, offering transparent, actionable insights for safer driving.