Advancements in sensor technologies have led to increased interest in detecting and diagnosing "driver states"-collections of internal driver factors generally associated with negative driving performance, such as alcohol intoxication, cognitive load, stress, and fatigue. This is accomplished using imperfect behavioral and physiological indicators that are associated with those states. An example is the use of elevated heart rate variability, detected by a steering wheel sensor, as an indicator of frustration. Advances in sensor technologies, coupled with improvements in machine learning, have led to an increase in this research. However, a limitation is that it often excludes naturalistic driving environments, which may have conditions that affect detection. For example, reductions in visual scanning are often associated with cognitive load (e.g., Angell et al., 2015); however, these reductions can also be related to novice driver inexperience (e.g., Stahl et al., 2019) and alcohol