  Analyzing the impact of the environment on drivers' stress level and workload
is of high importance for designing human-centered driver-vehicle interaction
systems and to ultimately help build a safer driving experience. However,
driver's state, including stress level and workload, are psychological
constructs that cannot be measured on their own and should be estimated through
sensor measurements such as psychophysiological measures. We propose using a
latent-variable state-space modeling framework for driver state analysis. By
using latent-variable state-space models, we model drivers' workload and stress
levels as latent variables estimated through multimodal human sensing data,
under the perturbations of the environment in a state-space format and in a
holistic manner. Through using a case study of multimodal driving data
collected from 11 participants, we first estimate the latent stress level and
workload of drivers from their heart rate, gaze measures, and intensity of
facial action units. We then show that external contextual elements such as the
number of vehicles as a proxy for traffic density and secondary task demands
may be associated with changes in driver's stress levels and workload. We also
show that different drivers may be impacted differently by the aforementioned
perturbations. We found out that drivers' latent states at previous timesteps
are highly associated with their current states. Additionally, we discuss the
utility of state-space models in analyzing the possible lag between the two
constructs of stress level and workload, which might be indicative of
information transmission between the different parts of the driver's
psychophysiology in the wild.
