Detecting emotions while driving remains a challenge in Human-Computer Interaction. Current methods to estimate the driver’s experienced emotions use physiological sensing (e.g., skin-conductance, electroencephalography), speech, or facial expressions. However, drivers need to use wearable devices, perform explicit voice interaction, or require robust facial expressiveness. We present VEmotion (Virtual Emotion Sensor), a novel method to predict driver emotions in an unobtrusive way using contextual smartphone data. VEmotion analyzes information including traffic dynamics, environmental factors, in-vehicle context, and road characteristics to implicitly classify driver emotions. We demonstrate the applicability in a real-world driving study (N = 12) to evaluate the emotion prediction performance. Our results show that VEmotion outperforms facial expressions by 29% in a person-dependent classification and by 8.5% in a person-independent classification. We discuss how VEmotion enables empathic car interfaces to sense the driver’s emotions and will provide in-situ interface adaptations on-the-go.

Music is a central instrument in video gaming to attune a player’s attention to the current atmosphere and increase their immersion in the game. We transfer the idea of scene-adaptive music to car drives and propose SoundsRide, an in-car audio augmented reality system that mixes music in real-time synchronized with sound affordances along the ride. After exploring the design space of affordance-synchronized music, we design SoundsRide to temporally and spatially align high-contrast events on the route, e. g., highway entrances or tunnel exits, with high-contrast events in music, e. g., song transitions or beat drops, for any recorded and annotated GPS trajectory by a three-step procedure. In real-time, SoundsRide 1) estimates temporal distances to events on the route, 2) fuses these novel estimates with previous estimates in a cost-aware music-mixing plan, and 3) if necessary, re-computes an updated mix to be propagated to the audio output. To minimize user-noticeable updates to the mix, SoundsRide fuses new distance information with a filtering procedure that chooses the best updating strategy given the last music-mixing plan, the novel distance estimations, and the system parameterization. We technically evaluate SoundsRide and conduct a user evaluation with 8 participants to gain insights into how users perceive SoundsRide in terms of mixing, affordances, and synchronicity. We find that SoundsRide can create captivating music experiences and positively as well as negatively influence subjectively perceived driving safety, depending on the mix and user.