Can AI Route You to Happiness? A Technical Study on Affective Automotive Navigation Interfaces

Conventional navigation systems, fixated on metrics such as time and distance, neglect the driver's emotional well-being, despite driving routes being inherent emotional triggers. This raises a critical question for the Intelligent User Interface community: How can intelligent systems successfully route information based on emotion? To address this gap, we introduce HappyRouting, an empathic car interface designed as an initial attempt to guide drivers through real-world traffic while actively optimizing for positive emotional states. Our core technical contribution is a machine learning-based emotion map layer that predicts the affective valence along various routes using both static and dynamic contextual data. HappyRouting enables the generation of "happy routes'' integrated into a functional vehicular interface prototype. We explored the efficacy of this approach in a preliminary, small-scale driving study (N=13). Our initial findings provide provocative evidence: Emotion-optimized routes successfully increased the subjectively perceived valence by 11% (p=.007) compared to standard routes. Furthermore, despite taking 1.25 times longer on average, participants consistently perceived the travel duration as shorter. This result suggests that integrating emotional optimization could fundamentally challenge the speed-first paradigm. However, recognizing the constraints of our initial, limited sample, we conclude by discussing ethical and computational challenges that must be resolved before emotion-based routing can be safely and scalably integrated into next-generation intelligent navigation apps.