The impact of automated planning aids on situation awareness and workload in the monitoring of uncrewed vehicles
Bibliographic record
Abstract
Uncrewed vehicles (UVs) are pervasive in civilian and military operations. While automated systems are increasingly used to plan and implement UV routes, human operators remain employed to monitor the UVs. Good situational awareness (SA) allows operators to recognize the need for intervention. While autonomous planning systems can reduce an operator’s workload, they may circumvent a critical opportunity to build SA resulting in poor monitoring performance. To evaluate whether route planning contributes to an operator’s ability to monitor UVs, participants completed a virtual UV monitoring task with assistance from an automated route planning system. The system operated at three levels of automation (LOA) where the generation and selection functions were traded off between a human operator and an automated planning system (between groups). Once a route was determined, participants monitored the UVs as they traveled their outlined routes and intervened if the UVs were approaching an unsafe area. SA, situation assessment (visual attention), perceived workload, and performance were evaluated between the three levels of the automated system. Experience with the task, but not LOA, affected these variables indicating that the automation of UV route planning functions may not be detrimental to operator SA during monitoring.
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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 | 0.012 |
| 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) | 0.001 | 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".