HappyRouting: Learning Emotion-Aware Route Trajectories for Scalable
  In-The-Wild Navigation
David Bethge Daniel Bulanda Adam Kozlowski Thomas Kosch Albrecht Schmidt Tobias Grosse-Puppendahl
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.