Routes represent an integral part of triggering emotions in drivers. Navigation systems allow users to choose a navigation strategy, such as the fastest or shortest route. However, they do not consider the driver's emotional well-being. We present HappyRouting, a novel navigation-based empathic car interface guiding drivers through real-world traffic while evoking positive emotions. We propose design considerations, derive a technical architecture, and implement a routing optimization framework. Our contribution is a machine learning-based generated emotion map layer, predicting emotions along routes based on static and dynamic contextual data. We evaluated HappyRouting in a real-world driving study (N=13), finding that happy routes increase subjectively perceived valence by 11% (p=.007). Although happy routes take 1.25 times longer on average, participants perceived the happy route as shorter, presenting an emotion-enhanced alternative to today's fastest routing mechanisms. We discuss how emotion-based routing can be integrated into navigation apps, promoting emotional well-being for mobility use.
View on arXiv@article{bethge2025_2401.15695,
title={ HappyRouting: Learning Emotion-Aware Route Trajectories for Scalable In-The-Wild Navigation },
author={ David Bethge and Daniel Bulanda and Adam Kozlowski and Thomas Kosch and Albrecht Schmidt and Tobias Grosse-Puppendahl },
journal={arXiv preprint arXiv:2401.15695},
year={ 2025 }
}