This research is a part of the IVAT (In-Vehicle Assistive Technology) project, an in-dash interface design project to help drivers who have various disabilities, including deficits in emotion regulation. While there have been several studies on emotion detection for drivers, few studies have seriously addressed what to detect and why. Those are crucial issues to consider when implementing an effective affect management system. Phase 1 of our study gathered a total of 33 different driving situations that can induce emotions and 56 plausible affective keywords to describe such emotions. Phase 2 analyzed factor structures of affect for driving contexts through user ratings and Factor Analysis, and obtained nine factors: fearful, happy, angry, depressed, curious, embarrassed, urgent, bored, and relieved. These factors accounted for 65.1% of the total variance. Results are discussed in terms of designing the IVAT emotion detection and regulation system for driving contexts.
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