National Advanced Driving Simulator Measures Driver Interactions with Automated Driving System
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Summary
This study, conducted at the National Advanced Driving Simulator (NADS) at the University of Iowa, investigates driver interactions with Level 3 automated driving systems, specifically focusing on the transfer of control from automation to manual driving. The research addresses critical human factors questions regarding how quickly drivers develop trust in automated systems and how effectively they perform when required to resume manual control. The study was part of the SAFER-SIM University Transportation Center project, funded by the U.S. Department of Transportation, aiming to ensure the safe use of automated vehicles by understanding the dynamics of conditional automation where drivers can shift physical and mental control to the system but must intervene when necessary. The experimental design utilized the high-fidelity NADS simulator to expose participants to various driving scenarios, including work zones, missing lane lines, elevated ramp curves, exit ramps, and slow lead vehicles. Two automation scenarios were tested: one with a highly capable system that could respond to most events autonomously, and another with a less capable system. To engage drivers during automated periods, a trivia task served as the primary activity. Takeover requests (TORs) were modeled after NHTSA research, featuring three alert stages: informational, cautionary, and imminent. Driver performance and comfort were measured as indicators of trust development. Data on speed and control were sampled every five seconds for up to a minute following manual takeovers to assess performance recovery. Key findings revealed that while drivers could physically take control within five seconds of a request, there was a significant 15-to-25-second "vulnerable window" before they returned to normal driving performance and gaze patterns. This delay confirms previous observations on the time required to regain situational awareness. The study identified three distinct comfort profiles regarding trust development: some drivers were immediately comfortable, others took a long time, and others adapted quickly. Performance differences emerged based on demographics and experience; women achieved lower minimum speeds than men, while men spent more time in manual mode. Younger drivers exhibited fewer steering reversals but greater lane position variability compared to older drivers. Additionally, drivers encountering the less capable system during their first drive showed higher-frequency steering adjustments compared to those who experienced it later. The significance of this research lies in its contribution to understanding the timeline of trust formation and the performance lag during control transfers. The identified 15-to-25-second vulnerable period highlights a critical safety gap that requires further investigation. These findings provide actionable insights for designers of automated driving systems, suggesting that interfaces should be engineered to promote an appropriate degree of trust and facilitate smoother transitions back to manual control. By clarifying how different user groups interact with automation, the study supports the development of safer human-machine interaction protocols for Level 3 automated vehicles.
Key finding
Drivers physically resumed control in under five seconds after a takeover request, but took 15 to 25 seconds to return to normal driving performance and gaze patterns.
Methodology
simulator
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 (7 acquisition events logged).
| Stage | Outcome | Tool | Model | Prompt | Attempts | Completed |
|---|---|---|---|---|---|---|
| discover | success | rosap | — | — | 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 | partial | — | — | — | 3 | 2026-06-10 |
Summary generated by qwen3.6-27b-prismaquant on 2026-06-10; verification: verified_with_issues.
Topics
Ranked by relevance to this paper. Hover a topic for its definition.
- automation
- takeover transitions
- trust calibration
- automation surprise
- automation complacency bias
- mode awareness
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: behavioral performance data
- Methodological Resource: measurement protocol
- Theoretical Contribution: conceptual framework