Automatically inferring drivers' emotions during driver-pedestrian interactions to improve road safety remains a challenge for designing in-vehicle, empathic interfaces. To that end, we carried out a lab-based study using a combination of camera and physiological sensors. We collected participants' (N=21) real-time, affective (emotion self-reports, heart rate, pupil diameter, skin conductance, and facial temperatures) responses towards non-verbal, pedestrian crossing videos from the Joint Attention for Autonomous Driving (JAAD) dataset. Our findings reveal that positive, non-verbal, pedestrian crossing actions in the videos elicit higher valence ratings from participants, while non-positive actions elicit higher arousal. Different pedestrian crossing actions in the videos also have a significant influence on participants' physiological signals (heart rate, pupil diameter, skin conductance) and facial temperatures. Our findings provide a first step toward enabling in-car empathic interfaces that draw on behavioural and physiological sensing to in situ infer driver emotions during non-verbal pedestrian interactions.

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doi.org/10.1145/3543174.3546842
14th International ACM Conference on Automotive User Interfaces and Interactive Vehicular Applications, AutomotiveUI 2022
Distributed and Interactive Systems

Rao, S., Ghosh, S., Pons Rodriguez, G., Röggla, T., El Ali, A., & César Garcia, P. S. (2022). Investigating affective responses toward in-video pedestrian crossing actions using camera and physiological sensors. In Main Proceedings - 14th International ACM Conference on Automotive User Interfaces and Interactive Vehicular Applications, AutomotiveUI 2022 (pp. 226–235). doi:10.1145/3543174.3546842