Emotion Regulation for Drivers
Aggressive driving behaviors and driver frustration cause accidents and fatalities. Current attempts by academic researchers and car manufacturers to combat driver frustration focus on detecting the emotional state of the driver, and then determining an appropriate reaction to that state. Recognizing driver emotion through facial, voice, and physiological cues is a difficult task in the due to light, noise, and individual differences in driving and expressive styles. Similarly, responding appropriately to affective state of the driver requires a careful implementation of a voice interface. The work presented in this dissertation targets driver frustration but shifts the technology burden away from capturing driver state to understanding the environment. Instead of studying interfaces that require precise knowledge of the driver state, the dissertation work presented here assumes that drivers will experience negative emotion based on common frustrations (e.g., traffic, being cut-off), and that the technology to detect these conditions is currently available. The dissertation study examines the effects of emotion regulation type (cognitive reappraisal vs. suppression), source (self vs. other), and timing (pre-drive vs. in-drive) on driver performance and emotional states in a between-participants design (N = 112). Regulation type strongly affected driver attitudes, but only had a minimal effect on measures of driving behavior. This suggests that the demand of the driving environment Implications for application and theory are discussed.