Effects of directional TOR interfaces on takeover performance: A protocol for a systematic review and meta-analysis
DOI: 10.21203/rs.3.rs-10507516/v1
archive: archived pipeline: cataloged verified
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Summary
This paper presents a protocol for a systematic review and meta-analysis designed to evaluate the effectiveness of directional Takeover Requests (TORs) in automated vehicles. The research is motivated by the operational limitations of SAE Level 2 and 3 automation, which require human drivers to regain control when the system exits its Operational Design Domain. While conventional TORs warn drivers of the need to intervene, they often fail to guide attention to specific safety-critical areas. Directional TORs, which indicate either the location of a hazard (“towards-hazard”) or a safe maneuvering area (“towards-free-lane”), have been proposed to improve takeover performance. However, existing empirical studies report inconsistent findings regarding whether directional TORs reduce takeover time or improve takeover quality (e.g., lateral acceleration) compared to non-directional warnings, and whether one directional strategy is superior to the other. This protocol aims to synthesize available evidence to resolve these inconsistencies and identify sources of heterogeneity. The methodology involves a comprehensive search of five databases (Scopus, Web of Science, ProQuest, ACM Digital Library, and Google Scholar) for studies published between January 2014 and May 2026. Eligible studies must involve SAE Level 2 or 3 automation, include a TOR transition, feature directionality as an independent variable, and measure takeover time or quality metrics in simulator or real-world settings. The initial search yielded 3,809 records, reduced to 3,472 after removing duplicates. The study selection process will involve independent screening by two reviewers, with disagreements resolved by a third. Data extraction will capture study characteristics, participant demographics, intervention specifics, and outcome metrics. Methodological quality and risk of bias will be assessed using the ROBINS-I tool. The analysis will employ meta-analytic techniques to calculate effect sizes, using mean differences or standardized mean differences for continuous outcomes and risk or odds ratios for dichotomous outcomes. Statistical heterogeneity will be evaluated using the Q-statistic and I² statistic. If significant heterogeneity is detected, subgroup analyses, sensitivity analyses, and meta-regression will be conducted to explore underlying causes. If meta-analysis is not feasible due to methodological variation, a narrative qualitative synthesis will be performed. The authors anticipate including approximately 30–40 studies. The significance of this work lies in its potential to clarify the optimal design of TOR interfaces for automated driving. By synthesizing conflicting evidence, the review aims to determine whether directional cues genuinely enhance driver performance and which specific directional strategies are most effective. This will provide evidence-based guidance for human-machine interface design, ultimately supporting safer transitions from automated to manual control. The protocol ensures transparency and reproducibility through adherence to PRISMA guidelines and rigorous quality assessment.
Provenance
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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 | pdftotext | — | — | 4 | 2026-08-10 |
| clean | success | clean | — | — | 2 | 2026-08-10 |
| chunk | success | chunk | — | — | 2 | 2026-08-10 |
| embed | success | embed | Qwen/Qwen3-Embedding-8B | — | 2 | 2026-08-10 |
| promote | success | — | — | — | 1 | 2026-08-09 |
| summarize | success | llm | qwen3.6-27b-nvidia | summ-v5 | 2 | 2026-08-10 |
| tag | success | vector_similarity | — | — | 17 | 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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Information type
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- Empirical Findings: behavioral performance data
- Methodological Resource: measurement protocol
- Synthesis & Review: quantitative synthesis