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Affective states have a critical role in driving performance and safety. Theycan degrade driver situation awareness and negatively impact cognitiveprocesses, severely diminishing road safety. Therefore, detecting and assessingdrivers' affective states is crucial in order to help improve the drivingexperience, and increase safety, comfort and well-being. Recent advances inaffective computing have enabled the detection of such states. This may lead toempathic automotive user interfaces that account for the driver's emotionalstate and influence the driver in order to improve safety. In this work, wepropose a multiview multi-task machine learning method for the detection ofdriver's affective states using physiological signals. The proposed approach isable to account for inter-drive variability in physiological responses whileenabling interpretability of the learned models, a factor that is especiallyimportant in systems deployed in the real world. We evaluate the models onthree different datasets containing real-world driving experiences. Our resultsindicate that accounting for drive-specific differences significantly improvesmodel performance.

Detection of Real-world Driving-induced Affective State Using Physiological Signals and Multi-view Multi-task Machine Learning