Abstract
Frustration while driving possibly causes accidents. Therefore, frustration-aware in-vehicle systems that help to mitigate frustration have gained increasing attention. Until now, little is known about effective interaction strategies. Recent studies indicate that voice interfaces enable natural human-machine interaction and thus could be valuable for in-vehicle frustration mitigation. Hence, this study investigates the effects of a frustration mitigation assistant via a voice-user interface as well as on its related user experience (UX) and users’ acceptance ratings. For this, a voice-user interface was designed that interacted with participants exposed to frustrating traffic situations during simulated driving. Frustration mitigation was adapted to these situations and based on well-established general emotion regulation theories. Participants (N = 13) took four drives in a driving simulator. Three drives served as distraction from the actual study goal while in the experimental drive participants were frustrated by goal-blocking traffic situations integrated in the simulation. In order to compare frustrating drives with and without voice interface, one half of the participants experienced the intervention of the voice-user interface (assisted group) while the other half drove without voice assistant (control group). After each drive the subjective frustration level was assessed via self-report. In addition, participants who experienced the voice interface were asked about their UX and acceptance. The frustration ratings indicate a tendency towards reduced frustration in the assisted compared to the control group. UX and acceptance ratings indicate positive experiences. Thus, this study provides first insights into the feasibility of assistants via voice-user interfaces for in-vehicle frustration mitigation.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Similar content being viewed by others
References
Lazarus, R.S.: Progress on a cognitive-motivational-relational theory of emotion. Am. Psychol. 46, 819–834 (1991). https://doi.org/10.1037/0003-066X.46.8.819
Bosch, E., Ihme, K., Drewitz, U., Jipp, M., Oehl, M.: Why drivers are frustrated: results from a diary study and focus groups. Eur. Transp. Res. Rev. 12(1), 1–13 (2020). https://doi.org/10.1186/s12544-020-00441-7
Jeon, M.: Towards affect-integrated driving behaviour research. Theoret. Issues Ergon. Sci. 16, 553–585 (2015). https://doi.org/10.1080/1463922X.2015.1067934
Berkowitz, L.: Frustration-aggression hypothesis: examination and reformulation. Psychol. Bull. 106, 59–73 (1989). https://doi.org/10.1037/0033-2909.106.1.59
Lee, Y.-C.: Measuring drivers’ frustration in a driving simulator. Proc. Hum. Factors Ergon. Soc. Ann. Meet. 54, 1531–1535 (2010). https://doi.org/10.1177/154193121005401937
European Commission: White Paper ‘Roadmap to a single European Transport Area - Towards a competitive and resource-efficient transport system’ (2011)
Löcken, A., Ihme, K., Unni, A.: Towards designing affect-aware systems for mitigating the effects of in-vehicle frustration. In: Boll, S., Löcken, A., Schroeter, R., et al. (eds.) Adjunct Proceedings, AutomotiveUI 2017: The 9th International ACM Conference on Automotive User Interfaces and Interactive Vehicular Applications, September 24–27, Oldenburg, Germany. Association for Computing Machinery, New York, pp. 88–93 (2017)
Oehl, M., Ihme, K., Drewitz, U., et al.: Towards a frustration-aware assistant for increased in-vehicle UX. In: Janssen, C.P., Donker, S.F., Chuang, L.L., et al. (eds.) Proceedings of the 11th International Conference on Automotive User Interfaces and Interactive Vehicular Applications: Adjunct Proceedings, pp. 260–264. ACM, New York (2019)
Zepf, S., Stracke, T., Schmitt, A., et al.: Towards real-time detection and mitigation of driver frustration using SVM. In: Wani, M.A. (ed.) 18th IEEE International Conference on Machine Learning and Applications: ICMLA 2019, Boca Raton, Florida, USA, 16–19 December 2019. Proceedings, pp. 202–209. IEEE, Piscataway (2019)
Requardt, A.F., Ihme, K., Wilbrink, M., et al.: Towards affect-aware vehicles for increasing safety and comfort: recognising driver emotions from audio recordings in a realistic driving study. IET Intel. Transport Syst. 14, 1265–1277 (2020). https://doi.org/10.1049/iet-its.2019.0732
Franz, O., Drewitz, U., Ihme, K.: Facing driver frustration: towards real-time in-vehicle frustration estimation based on video streams of the face. In: Stephanidis C., Antona M. (eds.) HCI International 2020 - Posters. HCII 2020. CCIS, vol. 1226, pp. 349–356. Springer, Cham (2020). https://doi.org/10.1007/978-3-030-50732-9_46
Ihme, K., Unni, A., Zhang, M., et al.: Recognizing frustration of drivers from face video recordings and brain activation measurements with functional near-infrared spectroscopy. Front. Hum. Neurosci. 12, 327 (2018). https://doi.org/10.3389/fnhum.2018.00327
Gross, J.J.: Emotion regulation: current status and future prospects. Psychol. Inq. 26, 1–26 (2015). https://doi.org/10.1080/1047840X.2014.940781
Harris, H., Nass, C.: Emotion regulation for frustrating driving contexts. In: Tan, D., Fitzpatrick, G., Gutwin, C., et al. (eds.) Conference Proceedings and Extended Abstracts/The 29th Annual CHI Conference on Human Factors in Computing Systems: CHI 2011, Vancouver, BC, May 7–12, 2011, p. 749. ACM, New York (2011)
Oehl, M., Lienhop, M., Ihme, K.: Mitigating frustration in the car: which emotion regulation strategies might work for different age groups? Paper in Proceedings of HCII 2021 (accepted)
Franke, T., Attig, C., Wessel, D.: A personal resource for technology interaction: development and validation of the affinity for technology interaction (ATI) Scale. Int. J. Hum. Comput. Interac. 35, 456–467 (2019). https://doi.org/10.1080/10447318.2018.1456150
Rendon-Velez, E., van Leeuwen, P.M., Happee, R., et al.: The effects of time pressure on driver performance and physiological activity: a driving simulator study. Transp. Res. Part F Traffic Psychol. Behav. 41, 150–169 (2016). https://doi.org/10.1016/j.trf.2016.06.013
Ihme, K., Dömeland, C., Freese, M., et al.: Frustration in the face of the driver. IS 19, 487–498 (2018). https://doi.org/10.1075/is.17005.ihm
Roidl, E., Frehse, B., Höger, R.: Emotional states of drivers and the impact on speed, acceleration and traffic violations - a simulator study. Accid. Anal. Prev. 70, 282–292 (2014). https://doi.org/10.1016/j.aap.2014.04.010
Homepage how.fm. https://www.how.fm/. Accessed 24 Mar 2021
Krohne, H.W., Egloff, B., Kohlmann, C.-W., et al.: Untersuchungen mit einer deutschen Version der ‘Positive and Negative Affect Schedule’ (PANAS). Diagnostica 42, 139–156 (1996)
Minge, M., Riedel, L., Thüring, M.: meCUE – Ein modularer Fragebogen zur Erfassung des Nutzungserlebens. In: Boll, S., Maaß, S., Malaka, R. (eds.) Mensch und Computer 2013: Interaktive Vielfalt, pp. 89–98. Oldenbourg Verlag, München (2013)
van der Laan, J.D., Heino, A., de Waard, D.: A simple procedure for the assessment of acceptance of advanced transport telematics. Transp. Res. Part C Emerg. Technol. 5, 1–10 (1997). https://doi.org/10.1016/S0968-090X(96)00025-3
Author information
Authors and Affiliations
Corresponding authors
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2021 Springer Nature Switzerland AG
About this paper
Cite this paper
Krüger, S., Bosch, E., Ihme, K., Oehl, M. (2021). In-Vehicle Frustration Mitigation via Voice-User Interfaces – A Simulator Study. In: Stephanidis, C., Antona, M., Ntoa, S. (eds) HCI International 2021 - Posters. HCII 2021. Communications in Computer and Information Science, vol 1421. Springer, Cham. https://doi.org/10.1007/978-3-030-78645-8_30
Download citation
DOI: https://doi.org/10.1007/978-3-030-78645-8_30
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-030-78644-1
Online ISBN: 978-3-030-78645-8
eBook Packages: Computer ScienceComputer Science (R0)