Change in Mental Models of ADAS in Relation to Quantity and Quality of Exposure [Fact Sheet]

NHTSA · 2023 · ROSA P / Safety Research Using Simulation (SAFER-SIM) University Transportation Center

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Summary

This study investigates how the quantity and quality of exposure to Advanced Driver Assistance Systems (ADAS) influence drivers’ mental models, trust, workload, and system usage. The research addresses a critical safety concern: drivers often lack accurate knowledge of ADAS capabilities and limitations, leading to misuse or mistrust, particularly during edge cases. Motivated by the need to improve safe operation of in-vehicle technology, the project examined whether exposure to specific types of driving events could accelerate the development of accurate mental models for Adaptive Cruise Control (ACC). The researchers conducted an experimental longitudinal study using a fixed-base driving simulator with a 330-degree field of view. Sixteen novice drivers, aged 21 to 54, who had no prior familiarity with ACC, were recruited and randomly assigned to one of two groups. The "Regular Group" encountered only routine edge-case events (e.g., slow-moving vehicles), while the "Enhanced Group" experienced both routine events and rare edge cases (e.g., a lead vehicle straddling two lanes). Participants completed four simulator sessions, spaced approximately one week apart, with each session containing five events. Following each session, surveys measured drivers’ trust, workload, and system knowledge to assess changes in their mental models. The results demonstrated that drivers’ understanding of ACC improved with increased experience for both groups, with mental model scores rising from the first to the fourth session. Crucially, the Enhanced Group consistently exhibited higher levels of understanding than the Regular Group across all sessions. This difference was evident as early as the end of the first session, indicating that exposure to rare edge cases fast-tracked mental model development. The Regular Group required four sessions to reach the level of system understanding that the Enhanced Group achieved after just one session. The findings suggest that rare edge-case events provide richer information about system limitations, thereby enhancing knowledge more effectively than routine exposure alone. The study concludes that accurate mental models are essential for the safe operation of ADAS and that targeted exposure to diverse situations, including rare edge cases, is an effective strategy for improving driver understanding. These findings have significant implications for driver training and consumer education, suggesting that controlled exposure to risky situations—such as through simulation or error-feedback approaches—can help novice users learn system limitations safely. While the study utilized a simulator to mitigate safety risks, the authors note that future work should explore alternative methods to provide such exposure in real-world contexts, as encountering rare edge cases naturally may not be practical or safe.

Key finding

Drivers exposed to rare ACC edge cases developed more accurate mental models of the system faster than those exposed only to routine events.

Methodology

simulator

Sample size: 16

Provenance

The full processing record for this entry. Every stage of this paper's journey through the pipeline is logged — what ran, with which tool and model, how many attempts it took, and when it last completed. Discovered via bulk_ingest_rosap on 2026-05-23 (6 acquisition events logged).

Summary generated by qwen3.6-27b-prismaquant on 2026-06-10; verification: verified.

Topics

Ranked by relevance to this paper. Hover a topic for its definition.

• situational awareness
• mental model of traffic
• trust calibration
• automation surprise
• novice drivers
• induced exposure

Information type

What kind of knowledge this paper contributes, grouped by family — independent of topic (what it is about) and method (how it was studied).

• Empirical Findings: observational prevalence
• Methodological Resource: tool software
• Theoretical Contribution: computational model