Transfer from Highly Automated to Manual Control: Performance & Trust
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Summary
This study investigates the human factors challenges associated with transferring control from highly automated (SAE Level 3) vehicles to manual driving, specifically focusing on driver performance and the development of trust. As semi-autonomous vehicles become prevalent, understanding how drivers regain situational awareness and stabilize vehicle control after automation disengagement is critical for safety. The research aimed to validate previous findings regarding takeover times, assess performance decrements during unexpected transfers, and determine how automation reliability and demographic factors influence driver trust and behavior. The researchers conducted a driving simulator study using the high-fidelity National Advanced Driving Simulator (NADS-1) with 20 licensed participants aged 18–55. The experimental design was a 2 (automation capability) x 2 (age) x 2 (gender) mixed design. Participants completed a practice drive followed by two 30-minute study drives. One drive featured "more-capable" automation that handled most events autonomously, while the other featured "less-capable" automation that issued takeover requests (TORs) for all events. To simulate distraction, participants engaged in trivia questions on an iPad during automated phases. Data collection included simulator metrics (vehicle state, driver inputs), eye-tracking data (gaze direction), and in-cab Likert scale surveys measuring comfort and trust. Key findings revealed that drivers required a significant period to stabilize after taking control. There was a 15- to 25-second interval between physical takeover and the return to normal driving performance, confirming prior observations that full stabilization can take up to 40 seconds. Trust profiles varied among participants, clustering into three distinct groups: those who were immediately comfortable, those who took time to build comfort, and those who remained uncomfortable. Performance metrics showed that younger drivers and males generally spent less time in manual mode and had faster gaze return times to the road. Additionally, less-capable automation resulted in higher steering reversal rates and greater standard deviation in lane position, indicating poorer vehicle control during takeovers. Gender and age significantly influenced minimum speed and steering behaviors during specific events, such as encountering slow lead vehicles or missing lane lines. The study concludes that transfers from high automation to manual control involve substantial performance decrements and extended stabilization periods, posing safety risks if TORs are issued with insufficient lead time. The results highlight that trust is not static but develops dynamically based on system reliability and individual differences. The findings imply that automation systems must account for the time required for drivers to regain situational awareness and that interface designs should mitigate the performance drop-offs observed during unexpected takeovers. These insights are crucial for designing safe semi-autonomous systems and establishing regulatory guidelines for automation handovers.
Key finding
Drivers required 15 to 25 seconds between physical takeover and the return to normal driving performance, with significant variations in trust development based on individual comfort profiles and automation reliability.
Methodology
simulator
Sample size: 20
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).
| 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 | 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.
- automation
- takeover transitions
- trust calibration
- automation surprise
- manual
- trust in automation foundations
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, self report data
- Theoretical Contribution: computational model