Effect of Partially Automated Driving on Mental Workload, Visual Behavior and Engagement in Nondriving-Related Tasks: A Meta-Analysis
Bibliographic record
Abstract
ObjectiveThe goal of this meta-analysis is to investigate the effect of partial automation on mental workload, visual behavior, and engagement in nondriving-related tasks.BackgroundThe literature on the human factors of operating partially automated driving offers mixed findings. While some studies show partial driving automation to result in suboptimal mental workload, others found it to impose similar levels of workload to the ones observed during manual driving. Likewise, while some studies evidence a marked increase in off-road glances when the automated system was engaged, other work has failed to replicate this pattern.Method41 studies involving 1482 participants were analyzed using the PRISMA approach.ResultsNo significant differences in mental workload were found between manual and partially automated driving, indicating no changes in mental workload between the two driving modes. A higher likelihood of glancing away from the forward roadway and engaging in nondriving-related tasks was found when the partially automated system was engaged.ConclusionAlthough the adoption of partial driving automation comes with some intended safety benefits, its use is also associated with an increased engagement in nondriving-related activities.ApplicationThese findings add to our understanding of the safety of partial automation and provide valuable information to Human Factors practitioners and regulators about the use and potential safety risks of using these systems in the real-world.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.008 | 0.024 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.009 | 0.025 |
| Bibliometrics | 0.004 | 0.004 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".