Configuring user information by considering trust threatening factors associated with automated vehicles

Abstract

Background The accelerated development of automated driving technology has raised the expectation that commercially available automated vehicles will be increasingly become ubiquitous. It has been claimed that automated vehicles are safer than conventional manual vehicles, leading to the expectation of fewer accidents. However, people expect not only better but also near-perfect machines. Given that accidents involving automated vehicle do occur and are highlighted by the media, negative reactions toward automated vehicles have increased. For this reason, it is critical to research human–machine interaction to develop suitable levels of trust between human users and newly introduced automated vehicle systems. Method We start this study by defining user distrust toward automated vehicles in terms of four types of trustthreatening factors (TTFs) along with trust-threatening situations. Next, with 30 volunteer participants, we conduct a survey and a humanin-the-loop experiment involving riding in a simulated automated vehicle and experiencing 21 distrust scenarios. Result In terms of the information configuration type suitable for alleviating the TTFs, the participants preferred to receive information on external object recognition for all TTFs in general with an average necessity level score of 24.2, which was 8.0 points higher on average than the scores of the other information configuration types. The haptic modality-based method was the least preferred compared to the other information configuration methods, namely visual and auditory. Conclusion In this study, we focused on participants’ subjective responses and complementary quantitative studies, and the results of these studies put together are expected to serve as a foundation for designing a user interface that can induce trust toward automated vehicle among users.

Is the automated vehicle “aware” of the pedestrian? Examining driving behavior adaptation as a cue to inform the passenger of a potential hazard

“…Previous research has examined various aspects and underlying determinants of passenger trust in automated vehicles, including the requirements and effects of feedback from the automated driving system via in-vehicle human-machine interfaces (HMIs; e.g., Colley et al, 2020;Feierle et al, 2020;Hartwich et al, 2021;Wintersberger et al, 2021;Yun & Yang, 2022;see Ekman et al, 2017 for a trust-based framework for HMI design), uncertainty displays (e.g., Beller et al, 2013;Helldin et al, 2013), automation malfunctions (e.g., Feierle et al, 2021), overtrust (e.g., Victor et al, 2018), driving behavior and style (e.g., Ekman et al, 2019;Mühl et al, 2020), and initial or introductory information (e.g., Forster et al, 2018;Körber et al, 2018) on self-reported passenger trust.…”