A quantitative model of takeover request time budget for conditionally automated driving
DOI: 10.1109/access.2025.3636074
archive: archived pipeline: cataloged verified
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Summary
This paper addresses the lack of comprehensive, generalized approaches for estimating the time budget required for human drivers to safely resume control in conditionally automated driving (Level 3 automation). While previous studies established that takeover time varies by situation and driver variables, no method existed to predict the necessary Takeover Request Time Budget (TORTB) in advance. The authors aim to develop a quantitative model that estimates this budget based on scenario complexity, individual driver characteristics, and interface assistance, thereby enhancing safety during critical transitions. The study utilized a driving simulator to test three distinct scenarios: a stationary vehicle on a highway at 130 km/h (S1), a highway exit at 50 km/h (S2), and a right turn at a country road intersection at 80 km/h (S3). Eighty-three participants were divided into three groups: Group 1 received a fixed 7-second TORTB without visual imagery; Groups 2 and 3 received variable TORTBs (ranging from 4 to 6 seconds based on scenario complexity and drive ordinal) without and with visual hazard imagery, respectively. Performance was measured using takeover time, situation awareness, workload, average lateral displacement, and maximum acceleration. Statistical analysis employed MANOVA and ANOVA to evaluate the effects of scenario, drive ordinal, and group conditions. Results indicated that a fixed 7-second budget was suitable for two of the three scenarios, whereas variable budgets often proved insufficient. Specifically, Groups 2 and 3 exhibited higher accident rates, increased lateral displacement, and higher maximum acceleration compared to Group 1, indicating poorer performance under reduced time budgets. Visual imagery assistance increased takeover time due to additional visual workload, suggesting that such aids must be accounted for in the time budget. Furthermore, drivers required more time for their first takeover experience compared to subsequent drives. The data confirmed that scenario complexity, particularly speed and traffic density, significantly impacts the required time budget. Based on these findings, the authors propose a mathematical formula to estimate the TORTB. This model integrates individual stimulus response time, driving experience, and scenario-specific requirements. The study concludes that TORTB should not be fixed but dynamically adjusted to ensure drivers have sufficient time to regain situation awareness and complete necessary maneuvers. The inclusion of visual assistance tools must also factor into the budget calculation, as they increase cognitive demand. This quantitative approach provides a framework for automated driving systems to issue takeover requests with appropriate timing, improving safety and maneuver success rates.
Key finding
A scenario-aware time-budget formula combining individual response time, experience, and scenario demand outperforms a fixed 7 s budget for conditional-automation takeovers.
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 discover_arxiv on 2026-05-04 (7 acquisition events logged).
| Stage | Outcome | Tool | Model | Prompt | Attempts | Completed |
|---|---|---|---|---|---|---|
| discover | success | author_sweep | — | — | 5 | 2026-05-28 |
| archive | success | — | — | — | 1 | 2026-05-04 |
| 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 | normalization | — | — | 2 | 2026-05-28 |
| promote | success | — | — | — | 1 | 2026-05-04 |
| summarize | success | llm | qwen3.6-27b-prismaquant | summ-v5 | 2 | 2026-06-10 |
| tag | success | vector_similarity | — | — | 17 | 2026-06-11 |
| verify | partial | — | — | — | 2 | 2026-06-10 |
Summary generated by qwen3.6-27b-prismaquant on 2026-06-10; verification: verified_with_issues.
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- Empirical Findings: behavioral performance data
- Methodological Resource: measurement protocol
- Theoretical Contribution: conceptual framework