Driver instruction for automated vehicles: Assessing the role of specific elements on learner motivation and mental model development

Feinauer, Sophie; Groh, Irene; Petzoldt, Tibor · 2022 · Crossref

DOI: 10.54941/ahfe1002482

archive: archived pipeline: cataloged verified

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Summary

This study addresses the critical need for effective user education as driving tasks become increasingly automated. While prior research confirms that education improves driver mental models and trust in automated vehicles, the specific instructional elements that foster learning success remain underexplored. Grounded in self-determination theory, the authors hypothesized that intrinsic motivation is crucial for learning outcomes. Consequently, the study investigated how two specific instructional elements—feedback (to satisfy competence needs) and choice in task completion (to satisfy autonomy needs)—influence learner motivation, mental model development, trust, and acceptance of Level 2 and Level 3 automated driving systems. The researchers conducted an online experiment with 193 participants, randomly assigned to one of four groups: a control group receiving standard information; a competence group receiving feedback on quiz answers; an autonomy group allowed to choose the order of reading sections; and a combination group receiving both feedback and choice. The instructional content covered human-machine interfaces, driver responsibilities, and system functionality. Data were collected immediately after instruction and at a two-week follow-up. Dependent variables included intrinsic motivation (measured via the Intrinsic Motivation Inventory), mental model quality (assessed through a 36-item knowledge questionnaire and 15 situational transfer items), trust (Automation Trust Scale), and acceptance (usefulness and satisfaction scales). Statistical analyses employed ANOVA with post-hoc Dunnett tests to compare experimental groups against the control. Results indicated that feedback significantly enhanced intrinsic motivation to learn compared to the control group, whereas choice alone did not. Regarding mental model formation, the competence group outperformed the control group, particularly at the two-week follow-up, suggesting that feedback helped participants retain information better over time. Although the competence group also showed higher descriptive scores in knowledge transfer to specific driving situations, these differences were not statistically significant. Trust in the automated vehicle increased significantly for the competence group relative to the control group at the follow-up measurement, likely mediated by improved mental models. However, no significant differences were found in vehicle acceptance across groups, although acceptance ratings generally increased over time for all participants. The study concludes that incorporating feedback into driver education for automated vehicles is an effective strategy to boost motivation, support long-term mental model retention, and foster trust. In contrast, providing choice in learning order did not yield significant benefits, potentially because the mandatory completion of all sections limited true autonomy. These findings suggest that educational concepts for automated driving should prioritize competence-supportive elements like feedback, which are easy to implement and offer tangible benefits for user adaptation to new technologies.

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Topics

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• acceptance adoption
• trust calibration
• automation
• situational awareness
• automation surprise
• learner drivers

Information type

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• Empirical Findings: self report data
• Theoretical Contribution: computational model, theory or model