Facing Driver Frustration: Towards Real-Time In-Vehicle Frustration Estimation Based on Video Streams of the Face
Autor(en): | Franz, O. Drewitz, U. Ihme, K. |
Herausgeber: | Stephanidis, C. Antona, M. |
Stichwörter: | Affect-aware vehicles; Automated facial expression analysis; Driver frustration; Driving simulator; Empathic systems; Facial Expressions; Facings; Human computer interaction; Pre-processing units; Real time capability; Real-time estimation; Street traffic control; Traffic congestion; User experience; User Modeling; Vehicle interface, User interfaces; Vehicles; Video streaming, Driver frustration | Erscheinungsdatum: | 2020 | Herausgeber: | Springer | Journal: | Communications in Computer and Information Science | Volumen: | 1226 CCIS | Startseite: | 349 | Seitenende: | 356 | Zusammenfassung: | Drivers frequently experience frustration when facing traffic jams, red lights or badly designed in-vehicle interfaces. Frustration can lead to aggressive behaviors and negative influences on user experience. Affect-aware vehicles that recognize the driver's degree of frustration and, based on this, offer assistance to reduce the frustration or mitigate its negative effects promise remedy. As a prerequisite, this needs a real-time estimation of current degree of frustration. Consequently, here we describe the development of a classifier that can recognize whether a frustrated facial expression was shown based on video streams of the face. For demonstration of its real-time capabilities, a demonstrator of a frustration-aware vehicle including the classifier, the Frust-O-Meter, is presented. The system is integrated into a driving simulator and consists of (1) a webcam, (2) a preprocessing unit, (3) a user model, (4) an adaptation unit and (5) a user interface. In the current version, a happy song is played once a high degree of frustration is detected. The Frust-O-Meter can form the basis for the development of frustration-aware vehicles and is foreseen to be extended to more modalities as well as more user need-oriented adaption strategies in the near future. © 2020, Springer Nature Switzerland AG. |
Beschreibung: | Conference of 22nd International Conference on Human-Computer Interaction, HCII 2020 ; Conference Date: 19 July 2020 Through 24 July 2020; Conference Code:242529 |
ISBN: | 9783030507312 | ISSN: | 18650929 | DOI: | 10.1007/978-3-030-50732-9_46 | Externe URL: | https://www.scopus.com/inward/record.uri?eid=2-s2.0-85088748103&doi=10.1007%2f978-3-030-50732-9_46&partnerID=40&md5=db7145b8e41c38e6a0d0c8f7b5b37a12 |
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geprüft am 18.05.2024