Take-Over Request in Highly Automated Driving: A Survey on Driving Experience and Emergency Operation Accuracy
DOI: 10.5057/isase.2020-c000033
archive: archived pipeline: cataloged verified
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Summary
This study addresses the critical challenge of facilitating safe transitions between manual and automated control in highly automated vehicles (HAVs). As automation advances, drivers must seamlessly switch from secondary tasks to vehicle handling during emergencies, a process prone to delays and errors if the driver is not sufficiently engaged. The research aims to determine whether driving state and frequency significantly impact the accuracy of Take-Over Requests (TOR) and to investigate how different secondary tasks affect takeover performance. By understanding driver states, preferences, and error patterns, the authors seek to inform the design of user interfaces that support effective vehicle handling. The researchers conducted an online survey using Google Forms, targeting 1,122 respondents aged 20 to 50 with at least one year of driving experience. Based on Rogers’ innovation diffusion theory, the target demographic consisted of early adopters and the early majority, who are primary users during the hybrid mode period of HAV adoption. The questionnaire collected data on basic demographics, driving frequency, driving states (including stress sources), types of operating errors in emergencies, and preferences for secondary tasks in HAVs. Data analysis employed Chi-square tests to evaluate the independence between variables, specifically assessing correlations between driving frequency, driving state, and emergency mistake probability. The results indicated no significant correlation between driving state and the probability of making mistakes in an emergency for either gender group. However, driving frequency was associated with driving state for female respondents, with higher frequency linked to increased anxiety and fatigue. The most common errors in dangerous situations were incorrect use of blinkers (25.84%) and steering in the wrong direction (24.51%), followed by confusing the accelerator with the brake (14.92%). Regarding secondary tasks, respondents preferred listening to music, sleeping, using smartphones, and playing games. Crucially, the study found that automated driving does not alleviate driving stress; the primary sources of anxiety remain traffic accidents and insufficient traffic information. The significance of these findings lies in their implications for TOR interface design. Since driving state does not predict error probability, designers cannot rely on monitoring driver state alone to ensure safe takeovers. Instead, systems must prioritize instant communication to address driver anxiety and information deficits, thereby reducing distrust. The high incidence of steering and signaling errors suggests that TOR systems must provide clear, contextual information to help drivers quickly orient themselves. The authors conclude that efficient multimedia displays are necessary to convey complex messages, enabling drivers to resume control with shorter reaction times and greater accuracy during urgent transitions.
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| Stage | Outcome | Tool | Model | Prompt | Attempts | Completed |
|---|---|---|---|---|---|---|
| discover | success | Crossref | — | — | 1 | 2026-08-09 |
| archive | success | canonical_url | — | — | 1 | 2026-08-09 |
| extract | success | cached | — | — | 4 | 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.
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- Empirical Findings: behavioral performance data, self report data
- Theoretical Contribution: conceptual framework