The Impact of a Priori Information on Drivers’ Mental Models, Attitudes, and Behavior in Interaction with Partial and Conditional Driving Automation

Research output: Contribution to journalResearch articleContributedpeer-review

Abstract

The introduction of automated driving features to the market will fundamentally change driver vehicle interaction. Because most drivers can be considered novices in regard to automated driving systems, it is likely that they do not have complete knowledge on these systems when they encounter them for the first time. For this reason, the present study assessed how information before the first drive affects mental models, behavior, and attitudes towards a combined partially and conditionally automated system. In a driving simulator study with 45 participants, we manipulated the completeness of the information provided before the first interaction with the system. One group, called the complete group, received all information on requirements for activation of the system and system limitations that were encountered in the subsequent thirty-minute simulated drive. A second group, called the incomplete group, only received information on one out of three requirements for activation and system limitations, respectively. Results indicate that, only after the drive with the system, mental models of the incomplete group were as good as mental models of the complete group before the drive. Furthermore, reliance behavior was supported by complete a priori information on the automated function. There were no effects for time to take over or take-over quality. These results emphasize the importance of driver education before the first automated drive to enhance mental models and trust among drivers of automated vehicles.

Details

Original languageEnglish
JournalInternational Journal of Human-Computer Interaction
Publication statusPublished - 2024
Peer-reviewedYes

External IDs

Mendeley e163c05f-caa0-381c-8c3d-cf79998f8f5b
ORCID /0000-0003-3162-9656/work/160479846

Keywords

Keywords

  • acceptance, Automated driving, mental model, trust