Driver State Monitoring: Manipulating Reliability Expectations in Simulated Automated Driving Scenarios
DOI: 10.1109/tits.2021.3050518
archive: archived pipeline: cataloged verified
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Summary
This study investigates the physiological and psychological responses of drivers in simulated Highly Automated Driving (HAD) scenarios, specifically examining how traffic complexity and induced reliability expectations affect driver state and trust. As HAD technology advances, monitoring driver availability for safe take-over requests is critical. The research aims to determine if varying traffic complexity and a non-driving related task (NDRT) modulate physiological arousal, and if manipulating expectations of automation reliability influences trust and physiological responses. The experiment utilized a mixed repeated-measures design with 27 participants divided into two groups: one induced with high reliability expectations and the other with low reliability expectations regarding the automated vehicle’s performance. Participants drove in a high-fidelity simulator through scenarios of increasing complexity: highway, interurban, urban low complexity, and urban high complexity. A mentally demanding verbal 2-back task was introduced during the highway segment, and a risky evasive maneuver scenario concluded the drive. Physiological data, including electrocardiogram (ECG) and electro-dermal activity (EDA), were recorded to measure arousal. Trust in automation was assessed using the Trust in Automated Systems Scale at three intervals. Results indicated that traffic complexity and task engagement significantly influenced physiological arousal. The 2-back task elicited the highest cardiac activity, characterized by increased heart rate, elevated LF/HF ratio, and reduced RMSSD, surpassing arousal levels in all other driving conditions, including the high-complexity urban and risk scenarios. EDA measures also showed increased skin conductance responses during the 2-back task and high-complexity urban driving compared to lower-complexity conditions. However, contrary to hypotheses, there were no significant differences in physiological arousal between the high and low reliability expectation groups. Regarding trust, the manipulation of expectations successfully modulated self-reported trust scores; the high reliability group reported increased trust and decreased distrust over time, while the low reliability group showed the opposite trend. The findings suggest that mental workload from NDRTs can generate higher physiological arousal than complex driving environments alone, highlighting the importance of monitoring cognitive load rather than just environmental complexity. While reliability expectations did not alter physiological arousal, they significantly impacted trust, indicating that trust and physiological state are distinct constructs. These results provide a methodological foundation for training machine learning classifiers to detect driver states, potentially improving the safety and appropriateness of take-over requests in future automated vehicles by accounting for both environmental demands and cognitive engagement.
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| Stage | Outcome | Tool | Model | Prompt | Attempts | Completed |
|---|---|---|---|---|---|---|
| discover | success | Crossref | — | — | 1 | 2026-08-09 |
| archive | success | unpaywall | — | — | 2 | 2026-08-09 |
| extract | success | cached | — | — | 3 | 2026-08-10 |
| clean | success | clean | — | — | 1 | 2026-08-09 |
| chunk | success | chunk | — | — | 1 | 2026-08-09 |
| embed | success | embed | Qwen/Qwen3-Embedding-8B | — | 1 | 2026-08-09 |
| enrich | success | semantic_scholar | — | — | 1 | 2026-08-09 |
| promote | success | — | — | — | 1 | 2026-08-09 |
| summarize | success | llm | qwen3.6-27b-nvidia | summ-v5 | 2 | 2026-08-10 |
| tag | success | vector_similarity | — | — | 10 | 2026-08-11 |
| verify | success | — | — | — | 2 | 2026-08-10 |
Summary generated by qwen3.6-27b-nvidia on 2026-08-10; verification: verified.
Topics
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- trust calibration
- automation
- automation surprise
- situational awareness
- mode awareness
- automation complacency bias
Information type
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- Empirical Findings: physiological data
- Methodological Resource: validation psychometrics
- Theoretical Contribution: theory or model