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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.

In the present study, human behavioral patterns have been analyzed using eye-tracking-based perception time, human response time, and the manner of driving-control-transition when a vehicle issues a control take-over request (TOR). The time for perceiving the TOR varied ranging between 1.84 s and 19.93 s. In 54% of all TOR events, drivers began manual driving with braking, in 33% with throttle application, and in 13% with steering inputs. The observations of this study are as follows: men initiate throttle operation earlier than women do when given TORs; NDRT tasks do not affect users' response; and visual-only warnings are not recommended for TORs.

Despite recent advancement in autonomous vehicles, limitations of autonomous driving system still remain. When the autonomous driving system encounters failure, it hands over dynamic driving task to the driver. In the event of a failure of the autonomous driving system, effective take-over request should be issued according to driver’s status to guarantee safety and comfort of a driver. To devise the optimal alert modalities for take-over request that can minimize the risk of failure in the autonomous driving system, the following factors were drawn out for the experimental design: 4 scenarios for take-over request through the case analysis of cause of autonomous driving disengagement; and verifiable vehicle alert modalities of visual, auditory, and tactile aspects for take-over request through the literature search and market research. Then, we have established a virtual reality simulator for experiment environment that can effectively identify driver’s status based on human factors. This study will find out effective multi-modality for take-over request through an analysis of statistically significant differences according to driver’s status based on human factors by drawing the quantitative/qualitative data based on scenarios for take-over request and combinations of vehicle alert modalities of visual, auditory, and tactile aspects obtained from the results of previous studies analyzed in this experiment environment.