Abstract
The growing capabilities of Intelligent vehicle interaction systems contribute to the intricacy of interaction methods, subsequently influencing drivers’ trust. This study comprehensively examines prevailing interaction modes of Intelligent vehicles, employing simulated driving experiments and evaluating participants’ trust levels using an automated trust scale across diverse interaction modes. The experiment introduces three specific scenarios: traffic congestion, traffic accidents, and pedestrian crossing. During the formal experiment, participants navigate a simulated urban route, encountering random instances of the specified scenarios during autonomous driving. The interaction system issues prompt when these situations arise. Four distinct interaction modes were crafted: baseline, visual, auditory, and audio-visual. The experimental findings reveal a notably heightened level of human-machine trust in the combined audio-visual interaction mode in contrast to voice and visual interactions. Furthermore, visual interaction is more effective in enhancing driver trust than auditory interaction. In visual interaction, the HUD-based visual interaction significantly elevates driver trust compared with dash-board and central control screen interfaces. This research offers insights for refining the interaction design of autonomous driving vehicles.
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References
Reimer, B.: Driver assistance systems and the transition to automated vehicles: a path to increase older adult safety and mobility? Public Policy Aging Rep. 24(1), 27–31 (2014)
SAE International. Automated driving levels of driving automation are defined in new SAE international standard. http://www.sae.org/misc/pdfs/automated_driving.pdf. Accessed 21 Nov 2023
Hancock, P.A., Nourbakhsh, I., Stewart, J.: On the future of transportation in an era of automated and autonomous vehicles. Proc. Natl. Acad. Sci. U.S.A. 116, 7684–7691 (2019)
Noah, B.E., Walker, B.N.: Trust calibration through reliability displays in automated vehicles. In: Proceedings of the Companion of the 2017 ACM/IEEE International Conference on Human-Robot Interaction, pp. 361–362. ACM (2017)
Azevedo-Sa, H., Jayaraman, S.K., Esterwood, C.T., Yang, X.J., Robert, L.P., Tilbury, D.M.: Real-time estimation of drivers’ trust in automated driving systems. Int. J. Soc. Robot. 13, 1911–1927 (2020)
Clement, P., et al.: Enhancing acceptance and trust in automated driving through virtual experience on a driving simulator. Energies 15(3), 781 (2022)
Manchon, J.B., Bueno, M., Navarro, J.: How the initial level of trust in automated driving impacts drivers’ behaviour and early trust construction. Transp. Res. Part F Traffic Psychol, Behav, 86, 281–295 (2022)
Qu, J., Zhou, R., Zhang, Y., Ma, Q.: Understanding trust calibration in automated driving: the effect of time, personality, and system warning design. Ergonomics 66(12), 2165–2181 (2023)
Itoh, M., Pacaux-Lemoine, M.-P.: Trust view from the human-machine cooperation framework. In: 2018 IEEE International Conference on Systems, Man, and Cybernetics (SMC), pp. 3213–3218. Miyazaki, Japan (2018)
Ekman, F., Johansson, M., Sochor, J.: Creating appropriate trust in automated vehicle systems: A framework for HMI design. IEEE Trans. Hum. Mach. Syst. 48, 95–101 (2018)
Gao, Z., Li, W., Liang, J., Pan, X., Xu, W., Shen, M.: Trust in automated vehicles. Adv. Psychol. Sci. 29(12), 2172–2183 (2021)
Merritt, S.M., Ilgen, D.R.: Not all trust is created equal: dispositional and history-based trust in humanautomation interactions. Hum. Factors J. Hum. Factors Ergonom. Soc. 50(2), 194–210 (2008)
French, B., Duenser, A., Heathcote, A.: Trust in Automation – A Literature Review. CSIRO, Australia (2018)
Zhang, C., Yin, G., Wu, Z.: Human-Machine Interface Research of Autonomous Vehicles Based on Cognitive Work Analysis Framework. In: Ahram, T., Karwowski, W., Vergnano, A., Leali, F., Taiar, R. (eds.) IHSI 2020. AISC, vol. 1131, pp. 99–103. Springer, Cham (2020). https://doi.org/10.1007/978-3-030-39512-4_16
Zhou, X., et al.: An evaluation method of visualization using visual momentum based on eye-tracking data. Int. J. Pattern Recognit. Artif. Intell. 32, 1850016:1–1850016:25 (2018)
Cauffman, S.J., Lau, M., Deng, Y., Cunningham, C., Kaber, D.B., Feng, J.: Research and design considerations for presentation of non-safety related information via in-vehicle displays during automated driving. Appl. Sci. 12(20), 10538 (2022)
Ulahannan, A., Jennings, P., Oliveira, L., Birrell, S.: Designing an adaptive interface: using eye tracking to classify how information usage changes over time in partially automated vehicles. IEEE Access 8, 16865–16875 (2020)
Wang, R., Dong, S., Xiao, J.: Research on human-machine natural interaction of intelligent vehicle interface design. J. Mach. Des. 36(2), 132–136 (2019)
Jian, J.-Y., Bisantz, A.M., Drury, C.G.: Foundations for an empirically determined scale of trust in automated systems. Int. J. Cogn. Ergon. 4, 53–71 (2000)
Wickens, C.D.: Multiple resources and mental workload. Hum. Factor 50(3), 449–455 (2008)
Acknowledgement
This study is supported by the Fundamental Research Funds for the National Natural Science Foundation of China (Grant No. 52205264) and Shanghai Pujiang Program (No. 21PJC032).
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Qi, B., Guo, Q., Liu, M. (2024). A Study on the Effects of Different Interaction Modalities on Driving Trust in Automated Vehicles. In: Krömker, H. (eds) HCI in Mobility, Transport, and Automotive Systems. HCII 2024. Lecture Notes in Computer Science, vol 14732. Springer, Cham. https://doi.org/10.1007/978-3-031-60477-5_13
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DOI: https://doi.org/10.1007/978-3-031-60477-5_13
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