Synthetic Vision Databases for Runway Incursion Avoidance
Bibliographic record
Abstract
<div class="htmlview paragraph">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 27<sup>th</sup>, 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: <ol class="list nostyle"> <li class="list-item"> <span class="li-label">1.</span> <div class="htmlview paragraph">Geographical Information Systems,</div></li> <li class="list-item"> <span class="li-label">2.</span> <div class="htmlview paragraph">Global Positioning Satellites (known as GPS),</div></li> <li class="list-item"> <span class="li-label">3.</span> <div class="htmlview paragraph">Advances in Symbol Generation technology,</div></li> <li class="list-item"> <span class="li-label">4.</span> <div class="htmlview paragraph">Increases in computing power deliverable at the cockpit.</div></li></ol>This paper discusses those technologies through the history of the projects that have put them into action.</div> <div class="htmlview paragraph">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.</div> <div class="htmlview paragraph">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.</div> <div class="htmlview paragraph">This paper shall introduce the composition and design of these databases, for a general audience.</div> <div class="htmlview paragraph">Finally, this paper will explore the current industry &amp; 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.</div>
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 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".