Preliminary Effect of Synthetic Vision Systems Displays to Reduce Low-Visibility Loss of Control and Controlled Flight Into Terrain Accidents
Bibliographic record
Abstract
An experimental investigation was conducted to study the effectiveness of Synthetic Vision Systems (SVS) flight displays as a means of eliminating Low Visibility Loss of Control (LVLOC) and Controlled Flight Into Terrain (CFIT) accidents by low time general aviation (GA) pilots. A series of basic maneuvers were performed by 18 subject pilots during transition from Visual Meteorological Conditions (VMC) to Instrument Meteorological Conditions (IMC), with continued flight into IMC, employing a fixed-based flight simulator. A total of three display concepts were employed for this evaluation. One display concept, referred to as the Attitude Indicator (AI) replicated instrumentation common in today's General Aviation (GA) aircraft. The second display concept, referred to as the Electronic Attitude Indicator (EAI), featured an enlarged attitude indicator that was more representative of a “glass display” that also included advanced flight symbology, such as a velocity vector. The third concept, referred to as the SVS display, was identical to the EAI except that computer-generated terrain imagery replaced the conventional blue-sky/brown-ground of the EAI. Pilot performance parameters, pilot control inputs and physiological data were recorded for post-test analysis. Situation awareness (SA) and qualitative pilot comments were obtained through questionnaires and free-form interviews administered immediately after the experimental session. Initial pilot performance data were obtained by instructor pilot observations. Physiological data (skin temperature, heart rate, and muscle flexure) were also recorded. Preliminary results indicate that far less errors were committed when using the EAI and SVS displays than when using conventional instruments. The specific data example examined in this report illustrates the benefit from SVS displays to avoid massive loss of SA conditions. All pilots acknowledged the enhanced situation awareness provided by the SVS display concept. Levels of pilot stress appear to be correlated with skin temperature measurements.
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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 | 0.000 | 0.002 |
| 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) | 0.002 | 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".