Change in Mental Models of ADAS in Relation to Quantity and Quality of Exposure

Pradhan, Anuj K.; Roberts, Shannon C; Pai, Ganesh; Zhang, Fangda; Horrey, William J · 2023 · ROSA P / AAA Foundation for Traffic Safety

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Summary

This study investigates how the frequency and quality of exposure to Adaptive Cruise Control (ACC) edge-case events influence novice drivers’ mental models, trust, workload, and disengagement behaviors. Motivated by evidence that drivers often possess incomplete or inaccurate mental models of Advanced Driver Assistance Systems (ADAS), leading to misuse or mistrust, the research aims to determine if simulated exposure to system limitations can improve driver understanding. The authors hypothesized that exposure to both routine and rare edge cases would yield better mental models than exposure to routine cases alone. The researchers conducted a longitudinal driving simulator study with 16 novice ACC users, randomly assigned to two groups: “Regular Exposure” (encountering routine edge-case events and non-events) and “Enhanced Exposure” (encountering both routine and rare edge-case events). Participants completed four simulator sessions, spaced approximately one week apart. Each session involved a drive containing five scenarios. The study utilized a mixed-design approach, with exposure frequency as a within-subject variable and exposure quality as a between-subject variable. Data were collected using a custom 54-item mental model survey (scoring accuracy and confidence), the Jian et al. trust survey, and the NASA Task Load Index for workload. Behavioral data included ACC disengagement rates and vehicle handling metrics (speed variability, lane offset, and acceleration) following disengagements. Results indicated that both exposure frequency and quality significantly improved drivers’ mental model scores. Mental model scores increased with each subsequent session, and the Enhanced Exposure group achieved significantly higher overall scores (mean = 8.1) compared to the Regular Exposure group (mean = 5.8). However, encountering rare edge cases had negative effects on other metrics: the Enhanced Exposure group reported significantly lower trust in the ACC system and higher ratings for mental demand and effort compared to the Regular Exposure group. There were no significant effects of exposure frequency or quality on ACC disengagement rates or post-disengagement vehicle handling behaviors. The findings suggest that while repeated exposure to ACC edge cases—particularly rare ones—effectively improves drivers’ understanding of system capabilities and limitations, it simultaneously reduces trust and increases perceived workload. This highlights a trade-off in ADAS training: methods that enhance mental models may initially cause driver discomfort or distrust. The authors conclude that while experience is critical for safe ADAS operation, challenges remain in providing drivers with appropriate exposure to system limits in a manner that does not negatively impact trust or acceptance. These results inform the development of safer training protocols and user interfaces for advanced vehicle technologies.

Key finding

Exposure to rare ACC edge cases improved drivers' mental models of the system but resulted in lower trust and higher workload compared to exposure to only 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

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

Information type

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• Empirical Findings: observational prevalence
• Theoretical Contribution: computational model, theory or model