  Detecting unsafe driving states, such as stress, drowsiness, and fatigue, is
an important component of ensuring driving safety and an essential prerequisite
for automatic intervention systems in vehicles. These concerning conditions are
primarily connected to the driver's low or high arousal levels. In this study,
we describe a framework for processing multimodal physiological time-series
from wearable sensors during driving and locating points of prominent change in
drivers' physiological arousal state. These points of change could potentially
indicate events that require just-in-time intervention. We apply time-series
segmentation on heart rate and breathing rate measurements and quantify their
robustness in capturing change points in electrodermal activity, treated as a
reference index for arousal, as well as on self-reported stress ratings, using
three public datasets. Our experiments demonstrate that physiological measures
are veritable indicators of change points of arousal and perform robustly
across an extensive ablation study.
