Trust and Distrust of Automated Parking in a Tesla Model X

Abstract

Objective The present study aims to evaluate driver intervention behaviors during a partially automated parking task. Background Cars with partially automated parking features are becoming widely available. Although recent research explores the use of automation features in partially automated cars, none have focused on partially automated parking. Recent incidents and research have demonstrated that drivers sometimes use partially automated features in unexpected, inefficient, and harmful ways. Method Participants completed a series of partially automated parking trials with a Tesla Model X and their behavioral interventions were recorded. Participants also completed a risk-taking behavior test and a post-experiment questionnaire that included questions about trust in the system, likelihood of using the Autopark feature, and preference for either the partially automated parking feature or self-parking. Results Initial intervention rates were over 50%, but declined steeply in later trials. Responses to open-ended questions revealed that once participants understood what the system was doing, they were much more likely to trust it. Trust in the partially automated parking feature was predicted by a model including risk-taking behaviors, self-confidence, self-reported number of errors committed by the Tesla, and the proportion of trials in which the driver intervened. Conclusion Using partially automated parking with little knowledge of its workings can lead to high degree of initial distrust. Repeated exposure of partially automated features to drivers can greatly increase their use. Application Short tutorials and brief explanations of the workings of partially automated features may greatly improve trust in the system when drivers are first introduced to partially automated systems.

Calibrating Trust in Automation Through Familiarity With the Autoparking Feature of a Tesla Model X

“…In conclusion, we have presented an early examination into real-world trust in automation, in an effort to build upon the burgeoning trust in automation body of work. Through manipulating the amount of information given to participants, we were able to observe significant differences in driver intervention rates, an observation we believe to be a clean measure of trust differences between conditions (de Visser, Pak, & Shaw, 2018; Tenhundfeld et al, 2019). These results also emphasize the importance of familiarity with how features like Tesla’s autoparking will function, to establish swift trust among its users (Meyerson, Weick, & Kramer, 1996).…”

“….” as defined by Lee and See (2004), then once the participant allows the system to help achieve the goal, their trust should be the same as others who similarly allow the system. However, as shown in Tenhundfeld et al (2019), there is not equal levels of self-reported trust even after participants all allow the system to achieve the desired end goal (in this case parking). Instead, the self-reported trust still correlates with earlier rates of intervention, even after the participants do not intervene (Tenhundfeld et al, 2019).…”

“…However, as shown in Tenhundfeld et al (2019), there is not equal levels of self-reported trust even after participants all allow the system to achieve the desired end goal (in this case parking). Instead, the self-reported trust still correlates with earlier rates of intervention, even after the participants do not intervene (Tenhundfeld et al, 2019). Our data also do not speak to how many of the participants would have caught an automation failure once trust had been established.…”

“…It is possible that their misunderstanding and initial distrust would present a barrier of use and adoption (Hancock et al, 2019;Norman, 2013). The second reason is that even when users are willing to let the system fully complete the autopark, their subjective self-reported level of trust still correlated with the frequency with which they intervened early in an experimental session (Tenhundfeld et al, 2019). One would expect that if trust in automation was as simple as "an attitude that an agent will help achieve an individual's goals.…”

“…This indicates that a certain level of familiarity in how the system performed the task was necessary for the participants to trust it. In addition, the initial hesitation to trust may have been attributable to nerves about damaging a vehicle that participants do not own (Tenhundfeld, de Visser, Ries, Finomore, & Tossell, 2019). If people were exposed to, or became familiar with, these systems through early exposure or brief introduction, it is conceivable that high initial intervention rates could potentially be reduced.…”