Change in Mental Models of ADAS in Relation to Quantity and Quality of Exposure
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Summary
This study investigates how the frequency and quality of exposure to Adaptive Cruise Control (ACC) influence drivers’ mental models, trust, workload, and disengagement behaviors. The research addresses a critical safety concern: drivers often possess incomplete or inaccurate mental models of Advanced Driver Assistance Systems (ADAS), leading to misuse, mistrust, or operational errors. The authors hypothesized that exposure to edge-case events—situations where ACC reaches its operational limits—could improve drivers’ understanding of system capabilities and limitations. 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 featuring five scenarios. The study utilized a mixed-subject design, with exposure frequency as a within-subject variable and exposure quality as a between-subject variable. Data were collected using surveys measuring mental models (a 54-item true/false survey with confidence ratings), trust (Jian et al. survey), and workload (NASA Task Load Index), alongside behavioral metrics such as ACC disengagement rates and vehicle handling stability. Results indicated that both exposure frequency and quality significantly improved drivers’ mental model scores. The Enhanced Exposure group achieved significantly higher mental model scores (mean = 8.1) compared to the Regular Exposure group (mean = 5.8). However, this improved understanding came with trade-offs in trust and workload. 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 was no significant effect of exposure frequency on trust. Regarding behavior, the Enhanced Exposure group disengaged ACC less frequently during routine events (15.6%) than the Regular Exposure group (25.8%), though they disengaged 20.3% of the time during rare events. No significant differences were found in post-disengagement vehicle handling metrics, such as speed variability or lane keeping. The findings suggest that while increased and varied exposure to ACC edge cases effectively improves drivers’ mental models and reduces unnecessary disengagements during routine scenarios, it also lowers trust and increases perceived workload. This highlights a complex relationship where better knowledge does not necessarily equate to higher trust. The study concludes that while experience is crucial for safe ADAS operation, challenges remain in providing drivers with appropriate exposure to rare edge cases in a safe manner without negatively impacting their trust in the technology. These insights are relevant for designing training programs and human-machine interfaces that balance knowledge acquisition with appropriate trust calibration.
Key finding
Exposure to rare ACC edge-case events significantly improves drivers' mental models of the system but results 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_aaa_foundation on 2026-05-23 (6 acquisition events logged).
| Stage | Outcome | Tool | Model | Prompt | Attempts | Completed |
|---|---|---|---|---|---|---|
| discover | success | aaa_foundation | — | — | 2 | 2026-05-23 |
| archive | success | — | — | — | 1 | 2026-05-23 |
| extract | success | cached | — | — | 2 | 2026-06-10 |
| clean | success | — | — | — | 1 | 2026-06-01 |
| chunk | success | — | — | — | 1 | 2026-06-01 |
| embed | success | — | — | — | 1 | 2026-06-02 |
| enrich | success | — | — | — | 1 | 2026-05-23 |
| promote | success | — | — | — | 1 | 2026-05-23 |
| summarize | success | llm | qwen3.6-27b-prismaquant | summ-v5 | 3 | 2026-06-10 |
| tag | success | vector_similarity | — | — | 19 | 2026-06-11 |
| verify | success | — | — | — | 2 | 2026-06-10 |
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
- trust calibration
- mental model of traffic
- automation surprise
- mode awareness
- 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
- Theoretical Contribution: computational model, theory or model