Determination of Takeover Time Budget Based on Analysis of Driver Behavior
DOI: 10.1109/ojits.2022.3224677
archive: archived pipeline: cataloged verified
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Summary
This study addresses the challenge of determining an appropriate takeover request (TOR) time budget for conditional driving automation (Level 3). Current systems often use arbitrary, fixed time budgets, which can lead to poor driver performance if the budget is too short (insufficient reaction time) or too long (delayed response or ignored warnings). The authors aim to establish a method for evaluating the suitability of specific time budgets based on driver behavior and scenario complexity, rather than seeking a single universal value. The researchers conducted a driving simulator experiment with 70 participants aged 19 to 41. The study utilized a constant TOR time budget of 7 seconds across eight distinct scenarios, categorized into four types (fixed obstacle, slow vehicle ahead, highway exit, and intersection turn) with two complexity levels each (varying ego vehicle speed from 50–130 km/h and presence of traffic agents). Participants performed three levels of non-driving related tasks (NDRTs) ranging from simple reading to proofreading aloud. The experimental design employed a randomized statistical approach to distribute scenario and task combinations among participants. Data collected included objective performance metrics such as takeover time (TOT), lateral displacement, and acceleration, as well as subjective measures of situation awareness and workload. The results indicate that driver performance is significantly influenced by scenario complexity and NDRT type. Drivers prioritized takeover effort based on relative speed, the presence of traffic agents, and junctions. Specifically, a 7-second time budget was found to be suitable for high-complexity scenarios involving vehicle speeds between 80 km/h and 130 km/h, a maximum of two traffic agents, three junctions, and hands-free secondary tasks. However, this budget was deemed too high for lower-complexity scenarios, leading to delayed responses. The study also found that weekly driving experience significantly correlated with faster takeover times and better situation awareness, whereas total years of licensing did not. The significance of this work lies in its demonstration that TOR time budgets should be adaptive rather than fixed. By analyzing the interdependencies between driver behavior, scenario variables, and secondary tasks, automated driving systems can dynamically budget sufficient time for warnings. This approach enhances safety by ensuring drivers receive adequate time for complex maneuvers while preventing unnecessary delays in simpler situations. The findings provide a foundation for developing algorithms that automatically analyze traffic scenes to determine optimal takeover time budgets, thereby improving the reliability and safety of conditional automation systems.
Key finding
A 7-second takeover time budget is suitable for high-speed scenarios with moderate traffic complexity and hands-free secondary tasks but is too long for lower complexity situations, indicating the need for adaptive time budgeting based on scenario variables.
Methodology
simulator
Sample size: 70
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 author_sweep_intake on 2026-05-28.
| Stage | Outcome | Tool | Model | Prompt | Attempts | Completed |
|---|---|---|---|---|---|---|
| discover | success | author_sweep | — | — | 2 | 2026-05-28 |
| archive | success | canonical_url | — | — | 11 | 2026-06-06 |
| extract | success | cached | — | — | 3 | 2026-06-10 |
| clean | success | clean | — | — | 1 | 2026-06-04 |
| chunk | success | chunk | — | — | 1 | 2026-06-04 |
| embed | success | embed | Qwen/Qwen3-Embedding-8B | — | 1 | 2026-06-04 |
| enrich | success | — | — | — | 1 | 2026-05-28 |
| promote | success | — | — | — | 1 | 2026-06-04 |
| summarize | success | llm | qwen3.6-27b-prismaquant | summ-v5 | 2 | 2026-06-10 |
| tag | success | vector_similarity | — | — | 15 | 2026-06-11 |
| verify | success | — | — | — | 2 | 2026-06-10 |
Summary generated by qwen3.6-27b-prismaquant on 2026-06-10; verification: verified.
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Information type
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- Empirical Findings: behavioral performance data
- Methodological Resource: measurement protocol
- Theoretical Contribution: conceptual framework