Modelling of unmanned aircraft visibility for see-and-avoid operations
Bibliographic record
Abstract
With more unmanned aircraft (UA) becoming airborne each day, an already high manned aircraft to UA exposure rate continues to grow. Pilots and rulemaking authorities realize that UA visibility is a real, but unquantified, threat to operations under the see-and-avoid concept. To finally quantify the threat, a novel contrast-based UA visibility model is constructed here using collected empirical data as well as previous work on the factors affecting visibility. This work showed that UA visibility <1300 m makes a midair collision a serious threat if a manned aircraft and a UA are on a collision course while operating under the see-and-avoid concept. Similarly, this work also showed that a midair collision may be unavoidable when UA visibility is <400 m. Validating pilot and rulemaking authority concerns, this work demonstrated that UA visibility distances <1300 and <400 m occur often in the real world. Finally, the model produced UA visibility lookup tables that may prove useful to rulemaking authorities such as the U.S. Federal Aviation Administration and International Civil Aviation Organization for future work in the proof of equivalency of detect and avoid operations. Until then, pilots flying at slower airspeeds in the vicinity of UA may improve safety margins.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".