Welcome to Fredrick Ekman's licentiate seminar!
Fredrick Ekman, MSc in Industrial Design Engineering is a doctoral student at Chalmers University of Technology, Sweden. In his work Fredrick Ekman investigates which factors affects trust in the human-machine interaction in self-driving vehicles (AD), and in which way. Building trust is a complex process and a fundamental piece of creating acceptance for AD-vehicles, as well as to create a positive experience for the user. The purpose of his PhD project is to create a better understanding regarding users' trust in automated systems, and the goal is to create guidelines that assists HMI designers in creating an appropriate level of trust in self-driving vehicles, with the vision to achieve a safer- and greater driving experience for the user. Interviews, questionnaires and observations are examples of methods used, to understand users trust for automated systems and vehicles.
Automated vehicles (AVs) have become a popular area of research due to, among others,
claims of increased traffic safety and user comfort. However, before a user can reap the
benefits, they must first trust the AV. Trust in AVs has gained a greater interest in recent
years due to being a prerequisite for user acceptance, adoption as well as important for good
user experience. However, it is not about creating trust in AVs, as much as creating an
appropriate level of trust in relation to the actual performance of the AV. However, little
research has presented a systematic and holistic approach that may assist developers in the
design process to understand what to primarily focus on and how, when developing AVs that
assist users to generate an appropriate level of trust.
This thesis presents two mixed-method studies (Study I and II). The first study considers what
factors affect users trust in the AV and is primarily based on a literature review as well as a
complementary user study. The second study, a user study, is built upon Study I and uses a
Wizard of Oz (WOz) approach with the purpose to understand how the behaviour of an AV
affects users trust in a simulated but realistic context, including seven day-to-day traffic
The results show that trust is primarily affected by information from and about the AV.
Furthermore, results also show that trust in AVs have primarily four different phases, before
the user’s first physical interaction with the AV (i), during usage and whilst learning how the
AV performs (ii), after the user has learned how the AV performs in a specific context (iii)
and after the user has learned how the AV performs in a specific context but that context
changes (iv). It was also found that driving behaviour affects the user’s trust in the AV during
usage and whilst learning how the AV performs. This was primarily due to how well the
driving behaviour communicated intentions for the users’ to be able to predict upcoming AV
actions. The users’ were also affected by the perceived benevolence of the AV, that is how
respectful the driving behaviour was interpreted by the user. Finally, the results also showed
that the user’s trust in the AV also is affected by aspects relating to different traffic situations
such as perceived task difficulty, perceived risk for oneself (and others) and how well the AV
conformed to the user’s expectations. Thus, it is not only how the AV performs but rather
how the AV performs in relation to different traffic situations.
Finally, since design research not only considers how things are, but also how things ought to
be, a tentative explanatory and prescriptive model was developed based on the results
presented above. The model of trust information exchange and gestalt explains how
information affecting user trust, travels from a trust information sender to a trust information
receiver and highlights the important aspects for developers to consider designing for
appropriate trust in AVs, such as the design space and related variables. The design variables
are a) the message (the type and amount of information), b) the artefact (the AV, including
communication channels and properties) and c) the information gestalt, which is based on the
combination of signals communicated from the properties (and communication channels). In
this case, the gestalt is what the user ultimately perceives; the combined result of all signals.
Therefore, developers need to consider not only how individual signals are perceived and
interpreted, but also how different signals are perceived and interpreted together, as a whole,
an information gestalt.