For driver-automation collaborative driving, accurately monitoring driver state
in smart cockpits is crucial for enhancing safety, comfort, and human-computer
interactions. However, existing research lacks clarity regarding the
relationships among driver states, and there is no consensus on the optimal
physiological channels to reliably capture these states. This study examined
three critical psychological constructs (i.e., perceived risk, trust in the
automated driving system, and driver fatigue) using a 37-participant driving
simulation experiment. We manipulated multiple factors to induce distinct driver
states among participants and recorded subjective scale ratings, heart rate
variability, galvanic skin response, and eye movement data. Subjective scale
ratings were adopted as the ground truth to examine the corresponding
measurement relationships between different physiological signals and the three
targeted dimensions of driver states. Our results proved that perceived risk,
trust, and fatigue were independent constructs and exhibited distinct and
significant associations with physiological metrics from corresponding
measurement channels. Specifically, perceived risk correlated with sympathetic
and parasympathetic activation, as reflected by heart rate variability metrics
such as standard deviation of normal-to-normal intervals and root mean square of
successive differences. Trust exhibited negative correlations with galvanic skin
response indicators of physiological arousal, including skin conductance level
and skin conductance responses, etc. Fatigue, meanwhile, showed consistent
correlations with eye movement metrics like percentage of eye closure and mean
fixation duration. These findings validate the specificity of physiological
metrics as objective indicators for each driver state construct, highlighting
their potential for real-time in-cabin monitoring, and contributes to improving
traffic safety and comfort of automated vehicles.