Abstract
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.
Original language | English |
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Title of host publication | AutomotiveUI '22 |
Subtitle of host publication | Proceedings of the 14th International Conference on Automotive User Interfaces and Interactive Vehicular |
Place of Publication | New York |
Publisher | Association for Computing Machinery (ACM) |
Pages | 226-235 |
Number of pages | 10 |
ISBN (Print) | 978-1-4503-9415-4 |
DOIs | |
Publication status | Published - 2022 |
Event | 14th International ACM Conference on Automotive User Interfaces and Interactive Vehicular Applications, AutomotiveUI 2022 - Seoul, Korea, Republic of Duration: 17 Sept 2022 → 20 Sept 2022 |
Conference
Conference | 14th International ACM Conference on Automotive User Interfaces and Interactive Vehicular Applications, AutomotiveUI 2022 |
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Country/Territory | Korea, Republic of |
City | Seoul |
Period | 17/09/22 → 20/09/22 |
Keywords
- driver emotion recognition
- empathic car
- pedestrian behaviour