  Affective states have a critical role in driving performance and safety. They
can degrade driver situation awareness and negatively impact cognitive
processes, severely diminishing road safety. Therefore, detecting and assessing
drivers' affective states is crucial in order to help improve the driving
experience, and increase safety, comfort and well-being. Recent advances in
affective computing have enabled the detection of such states. This may lead to
empathic automotive user interfaces that account for the driver's emotional
state and influence the driver in order to improve safety. In this work, we
propose a multiview multi-task machine learning method for the detection of
driver's affective states using physiological signals. The proposed approach is
able to account for inter-drive variability in physiological responses while
enabling interpretability of the learned models, a factor that is especially
important in systems deployed in the real world. We evaluate the models on
three different datasets containing real-world driving experiences. Our results
indicate that accounting for drive-specific differences significantly improves
model performance.
