Minimum Time to Situational Awareness During Transfer of Control Under Varying Levels of Task Load
Bibliographic record
Abstract
Technology advancements in the past two decades have made the human-vehicle connection stronger than ever. Since 2009 there has been a boom in the development of autonomous vehicles (AVs) as an increasing number of manufacturers have begun to see immense potential in this area of artificial intelligence (AI). While the private sector is racing forward with the development of autonomous features, there is a need to understand how the human driver will interface with these features before Level 5 automation is finally achieved. This study sought to explore how distractions during automated driving impacted hazard anticipation upon re-taking control. Twenty-one participants drove in a simulated environment across eight different scenarios to compare how four different in-vehicle tasks that were performed during automated driving affected hazard anticipation after re-taking manual control of the vehicle. An alert of a potential hazard was provided to drivers 6 seconds in advance of the hazard materializing, and the participants were instructed to disengage automation and take back control of the vehicle. The visual and audible tasks elicited a much higher workload than the control group, as captured by the NASA-TLX questionnaire, and drivers who performed the visual task spent, on average, 30 more seconds glancing away from the road during automated driving. Despite all this, there was no statistically significant difference in the hazard anticipation between the groups who performed an in-vehicle task and the control groups, suggesting that a 6-second warning time is sufficient for drivers to regain spatial awareness after a period of automated driving.
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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 | 0.001 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".