Fifty shades of frustration: Intra- and interindividual variances in expressing frustration. (April 2023)

• Record Type: Journal Article
• Authors: Bosch, Esther; Käthner, David; Jipp, Meike; Drewitz, Uwe; Ihme, Klas
• Is Part Of: Transportation research. Volume 94(2023)
• Page Start: 436
• Page End: 452
• Publication Date: 2023-04
• Subjects: Facial expression of emotion -- Interindividual variance -- Emotion recognition -- Affective computing -- Empathic vehicles -- Frustration recognition; Automobile drivers -- Psychology -- Periodicals; Automobile driving -- Psychological aspects -- Periodicals; Transportation -- Psychological aspects -- Periodicals; 629.283019
• DOI: 10.1016/j.trf.2023.03.004
• Languages: English
• ISSNs: 1369-8478

Abstract

Highlights: Frustration hinders safety in manual and acceptance in automated driving. Its automated recognition enables frustration-aware systems to mitigate frustration. This work confirms previously described facial expressions of frustration. To add, it reveals the presence of individual-typical expressions of frustration. Frustration-aware systems can benefit from considering these individual differences. Abstract: Negative emotions like frustration can lead to risky driving behavior in manual driving and may hinder the acceptance of innovative automated vehicles. The option to automatically recognize such negative emotions of vehicle users with so called affect-aware systems has gained increasing attention within the last few years. This offers the possibility to adapt vehicle functions, such as the human–machine interface, in real-time. Emotional expressions in face and body form potential indicators for user frustration. Previous studies have investigated expressions of frustration in the context of driving and mobility, but have neglected situational and interindividual differences. In this paper, we examined the possibility to improve the recognition of frustration by considering individual differences. For this, a driving simulator study with 50 participants and a real-world driving study with 23 participants were conducted. An analysis of participants' facial expressions during frustrating driving situations confirms previously reported expressions of frustration (Brow Lowerer, Dimpler, Brow Raiser, Smile and Lip Press). In addition, the results also hint towards high variance between and low variance within participants for all other expressions, suggesting the existence of individual-typical expressions of frustration. Hence, future frustration-aware systems could benefit from considering these individual differences by using a universally trained algorithm that is then customized towards each individual.