UAS integration in congested terminal airspace: challenges posed to pilots
Bibliographic record
Abstract
There is increasing demand to operate unmanned aircraft systems (UAS) in congested terminal environments, such as busy commercial airports. With this demand comes challenges to pilots. To identify these challenges, we conducted critical decision method (CDM) interviews with pilots. CDM is a cognitive task analysis method aimed at uncovering tacit cognitive challenges. Eight pilots from the U.S. were interviewed including four UAS pilots and four commercial pilots. Interviews were analyzed using thematic analysis, resulting in the identification of four categories of cognitive challenges: (i) noticing anomalies, (ii) diagnosing automation behavior, (iii) understanding when and how to intervene, and (iv) coordinating with air traffic control. In this paper, we describe each challenge, highlight real-world examples from our interviews, and provide some recommendations for addressing the implications of integrating UAS in congested terminal airspace.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| 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 | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.001 |
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 teacher head, 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".