Synthetic Vision Databases for Runway Incursion Avoidance
Bibliographic record
Abstract
For a number of years, we have encountered the problems with aircraft runway incursions, and ground vehicle runway incursions. Following the tragic accident at Tenerife on March 27th, 1977, involving a KLM 747 and Pam Am 747 airliners colliding in the fog, the industry has striven to improve the safety of ground operations or airport surface movements. A number of enabling technologies have been emerging that has made that possible. These include the following: 1. Geographical Information Systems, 2. Global Positioning Satellites (known as GPS), 3. Advances in Symbol Generation technology, 4. Increases in computing power deliverable at the cockpit. This paper discusses those technologies through the history of the projects that have put them into action. Industry trends have integrated those individual projects into a broader navigation paradigm, known as “Synthetic Vision”. This new idea seeks to broadly enhance the situational awareness of all phases of flight, including taxi, take-off, departure, enroute, arrival, approach, and finally landing, rollout, and taxi back to the gate. Synthetic Vision Systems (SVS) offer the hope of providing a significant decrease, or the eventual elimination of most types of runway incursions, at all of those airports for which synthetic vision databases shall be constructed. This paper shall introduce the composition and design of these databases, for a general audience. Finally, this paper will explore the current industry & regulatory developments, concluding with the emergence of commercial projects that seek to bring Runway Incursion Avoidance technology into the realm of commercial-off-the-shelf (COTS) solutions.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.021 | 0.017 |
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".