Effect of Partially Automated Driving on Mental Workload, Visual Behavior and Engagement in Nondriving-Related Tasks: A Meta-Analysis
DOI: 10.1177/00187208251323132
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Summary
This meta-analysis investigates the impact of partially automated driving (SAE Level 2) on driver mental workload, visual behavior, and engagement in nondriving-related tasks (NDRTs). The study addresses conflicting findings in existing literature regarding whether automation reduces cognitive demand or alters attention allocation. While some prior research suggests automation lowers workload, other studies indicate no change or even increased demand due to system supervision requirements. Similarly, evidence regarding visual attention is mixed, with some studies reporting increased off-road glances and others finding no significant changes. The authors aim to synthesize these disparate results to clarify the human factors implications of Level 2 automation, which is projected to dominate the market by 2025. The researchers conducted a systematic review and meta-analysis following the PRISMA framework, analyzing 41 empirical studies involving 1,482 participants. The search covered five databases for peer-reviewed articles published between 2010 and 2024. Inclusion criteria required studies to compare manual driving with partially automated driving using human participants, excluding simulations without real drivers, Level 3 automation, and studies testing novel interventions. The analysis incorporated physiological metrics (e.g., EEG, heart rate), behavioral measures (e.g., detection response tasks), and subjective reports (e.g., NASA-TLX) to assess mental workload. Visual behavior was measured via gaze duration and off-road glances, while NDRT engagement included activities like phone use or passenger interaction. Statistical analyses used Standardized Mean Changes (SMCs) within a multivariate random-effects model to account for within-subject designs and dependency among effect sizes. The results indicate no significant difference in mental workload between manual and partially automated driving modes. This finding suggests that the cognitive demands of supervising an automated system are comparable to those of manual operation, contradicting the hypothesis that automation significantly frees up mental resources. However, the analysis found a significantly higher likelihood of drivers glancing away from the forward roadway and engaging in NDRTs when the automated system was active. This shift in visual behavior and task engagement occurred despite the lack of change in overall mental workload, indicating that drivers reallocate their attention rather than reducing their total cognitive load. These findings imply that while partially automated driving does not reduce mental workload, it facilitates distraction by encouraging drivers to look away from the road and perform secondary tasks. This behavior poses safety risks, as drivers may disengage from the driving environment despite the system’s requirement for continuous supervision. The study concludes that the safety benefits of partial automation are counterbalanced by increased engagement in nondriving activities. These insights provide critical evidence for human factors practitioners and regulators, highlighting the need for interventions that mitigate distraction and ensure drivers maintain adequate situational awareness when using Level 2 systems.
Key finding
Partially automated driving does not significantly change mental workload compared to manual driving but significantly increases the likelihood of glancing away from the road and engaging in nondriving-related tasks.
Methodology
meta_analysis
Sample size: 1482
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 openalex_abstract on 2026-05-08 (2 acquisition events logged).
| Stage | Outcome | Tool | Model | Prompt | Attempts | Completed |
|---|---|---|---|---|---|---|
| discover | success | — | — | — | 1 | 2026-05-07 |
| archive | success | openalex | — | — | 7 | 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 | openalex | — | — | 2 | 2026-05-08 |
| promote | success | — | — | — | 1 | 2026-05-07 |
| 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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- Empirical Findings: behavioral performance data
- Theoretical Contribution: theory or model, conceptual framework