Abstract

Emotion monitoring in driving is important. Emotions can affect attention, memory, and decision-making and have a significant impact on our driving behaviors and safety. However, measuring and interpreting emotions is challenging: The same emotion can have different manifestations and different emotions can have similar manifestations. Contextualizing emotions can help with the interpretation and translation of emotional states. However, research on context and drivers' emotional states is limited. We investigate the effect of time, area, weather, surrounding conditions, and traffic conditions on drivers' emotions. Sixty-four images of various driving scenarios were generated using DALL•E 2, a generative AI model, and 238 participants were recruited through Prolific to respond how they would feel driving in such contexts. The results showed that rainy weather, tumultuous surrounding, and high traffic conditions were associated with an increase in negative emotions. On the other hand, driving in rural areas, in the morning time or with no traffic increased the intensity of positive emotions, while rainy weather conditions increase the intensity of negative emotions. The findings can guide the development of driver monitoring systems with respect to the effect of driver's emotional states.