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Analyzing the impact of the environment on drivers' stress level and workloadis of high importance for designing human-centered driver-vehicle interactionsystems and to ultimately help build a safer driving experience. However,driver's state, including stress level and workload, are psychologicalconstructs that cannot be measured on their own and should be estimated throughsensor measurements such as psychophysiological measures. We propose using alatent-variable state-space modeling framework for driver state analysis. Byusing latent-variable state-space models, we model drivers' workload and stresslevels as latent variables estimated through multimodal human sensing data,under the perturbations of the environment in a state-space format and in aholistic manner. Through using a case study of multimodal driving datacollected from 11 participants, we first estimate the latent stress level andworkload of drivers from their heart rate, gaze measures, and intensity offacial action units. We then show that external contextual elements such as thenumber of vehicles as a proxy for traffic density and secondary task demandsmay be associated with changes in driver's stress levels and workload. We alsoshow that different drivers may be impacted differently by the aforementionedperturbations. We found out that drivers' latent states at previous timestepsare highly associated with their current states. Additionally, we discuss theutility of state-space models in analyzing the possible lag between the twoconstructs of stress level and workload, which might be indicative ofinformation transmission between the different parts of the driver'spsychophysiology in the wild.

Driver State Modeling through Latent Variable State Space Framework in the Wild