An online-survey on user expectations and mental model of automated driving: The effects of automation description and technology readiness

Abstract: The European Hi-Drive project (https://www.hi-drive.eu/) is dedicated to overcoming the technological and societal challenges associated with the successful deployment of Automated Driving (AD). Hereby, a key focus is on creating user-friendly AD systems that prioritize driver safety. To better understand user’s mental models of AD as well as their expectations regarding system features, a questionnaire was developed within the project. In this questionnaire, respondents are presented with statements about AD and tasked with determining the accuracy of each statement. Additionally, participants are asked to indicate their confidence level in their judgment. The questionnaire was applied in an online survey including 211 participants. The survey compared two widely used taxonomies of Driving Automation (i.e., by SAE and BASt) as the basic description of AD. Participants were categorized into three groups based on their responses to a technology readiness questionnaire. Participants showing a high level of technology readiness were younger and indicated a higher level of experience with on-market ADAS. Furthermore, they showed a better understanding of AD and an overall more favorable evaluation of AD. There was not difference in the understanding of AD between the group who was instructed with the SAE taxonomy and the BASt taxonomy. While both descriptions effectively conveyed a foundational understanding of automated driving, they fell short in adequately communicating AD handling. Especially, items dealing with activation / deactivation and availability of AD and split of responsibility between AD and driver showed a low proportion of correct answers. If it comes to the expectation towards AD as well as its potentials, the majority of user expected AD to include features like stop and go traffic or automated lane changes. The expectations of participants were ambiguous about the AD being able to handle complex road infrastructure and they did not expect AD to operate in high speeds or adverse weather. The implications of the results are discussed for AD development and especially user training of AD. User expectations and their mental model of AD should be the basis for the development of scientifically sound user education of AD. The results of the present survey can help developers of AD to prioritize the most relevant system features based on actual user expectations.

Keywords: Automated Driving, Mental Model, Technology Readiness

DOI: 10.54941/ahfe1005214

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