HappyRouting: Learning Emotion-Aware Route Trajectories for Scalable In-The-Wild Navigation
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| Main Authors: | , , , , , |
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| Format: | Preprint |
| Published: |
2024
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| _version_ | 1866913817532825600 |
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| author | Bethge, David Bulanda, Daniel Kozlowski, Adam Kosch, Thomas Schmidt, Albrecht Grosse-Puppendahl, Tobias |
| author_facet | Bethge, David Bulanda, Daniel Kozlowski, Adam Kosch, Thomas Schmidt, Albrecht Grosse-Puppendahl, Tobias |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2401_15695 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | HappyRouting: Learning Emotion-Aware Route Trajectories for Scalable In-The-Wild Navigation Bethge, David Bulanda, Daniel Kozlowski, Adam Kosch, Thomas Schmidt, Albrecht Grosse-Puppendahl, Tobias Human-Computer Interaction Machine Learning 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. |
| title | HappyRouting: Learning Emotion-Aware Route Trajectories for Scalable In-The-Wild Navigation |
| topic | Human-Computer Interaction Machine Learning |
| url | https://arxiv.org/abs/2401.15695 |