Design And Evaluation Of A General Aviation PFD Symbology Based On Human Factors
Bibliographic record
Abstract
Throughout the world, more than 46% of the overall accidents occurring in the general aviation sector occur during the approach/landing phase of flight. According to the National Transportation Safety Board (NTSB), in 2018, more than 59% of these accidents occurred during the landing phase due to collision with terrain/or an object, power loss, and loss of control. This research focuses on reducing human-made errors by designing a new Primary Flight Display (PFD) symbology in an aircraft cockpit display using a Human Machine Interface (HMI) tool. The symbology is developed using Hierarchical Task Analysis (HTA), which simplifies the overall process into various subtasks and builds algorithms to solve the problem. Ryerson Fixed Base Flight Simulator (FBFS) is used to deploy and test the effectiveness of the newly proposed symbology against existing primary flight displays (analog and digital). Finally, human factor evaluation techniques are used to understand the mental and physical workload experienced by pilots using all three displays. Results showed an almost 62% decrease in mental demand and a 53% decrease in physical demand while using the new display symbology.
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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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 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 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".