Driver drowsiness is a leading and highly preventable cause of road accidents worldwide. This thesis therefore asks: Can smartwatch data accurately classify four distinct drowsiness levels with at least 90% accuracy in realistic driving conditions? Can we use this information to detect and prevent driver drowsiness? To investigate this topic, we analyze a dataset composed of 169,466 samples recorded with commercial smartwatch sensors. Each measurement combines Heart Rate (HR), Heart Rate Variability (HRV), and Respiration Rate (RR) with a synchronised arousal label that is discretised, via equal-width binning, into four classes spanning from full alertness to pronounced drowsiness. Our principal methodological innovation is a lightweight real-time processing system that streams incoming data through a sliding window, performs real-time feature scaling, and feeds the resulting features directly to the classifier.