Task Analysis and Predictive Workload Modeling for Autonomous Aircraft
Bibliographic record
Abstract
The desire to transition to single-pilot operations (SPO) has led to research and development of autonomous technologies that can take over tasks normally handled by two pilots and create a new paradigm that supports SPO. To safely achieve single-pilot operations (SPO) of an existing dual crew aircraft, the workload split between the two pilots needs to be analyzed and candidate tasks for offloading identified. Simulation is a valuable tool to model different task allocation strategies for such systems. This paper presents the methodology that was used to analyze shared tasks between a two-pilot crew and identify candidate tasks that could be handled by the autonomous system. A simulation tool called Improved Performance Research Integration Tool (IMPRINT), developed by the U.S. Army was used as part of the design process for an autonomous flight control system. IMPRINT was used to guide cognitive walk-throughs and model pilot workload to inform task allocation between autonomy and the human operator. Advantages and disadvantages of this method will be discussed as well as recommendations for future work.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| 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".