AutoTherm: A Dataset and Ablation Study for Thermal Comfort Prediction in Vehicles
State recognition in well-known and customizable environments such as
vehicles enables novel insights into users and potentially their intentions.
Besides safety-relevant insights into, for example, fatigue, user
experience-related assessments become increasingly relevant. As thermal comfort
is vital for overall comfort, we introduce a dataset for its prediction in
vehicles incorporating 31 input signals and self-labeled user ratings based on
a 7-point Likert scale (-3 to +3) by 21 subjects. An importance ranking of such
signals indicates higher impact on prediction for signals like ambient
temperature, ambient humidity, radiation temperature, and skin temperature.
Leveraging modern machine learning architectures enables us to not only
automatically recognize human thermal comfort state but also predict future
states. We provide details on how we train a recurrent network-based classifier
and, thus, perform an initial performance benchmark of our proposed thermal
comfort dataset. Ultimately, we compare our collected dataset to publicly
available datasets.