A Framework for Assessing Aircraft Detect and Avoid Systems Using Monte Carlo Simulations
Bibliographic record
Abstract
The rapidly expanding use of remotely piloted aircraft systems (RPAS) in airspace historically dominated by traditional aviation has increased the potential for collisions.This issue, combined with the evolving Beyond Visual Line of Sight (BVLOS) regulations, highlights the critical importance of robust Detect and Avoid (DAA) systems.The Monte Carlo simulation framework presented evaluates different cooperative and non-cooperative DAA systems onboard RPAS ownships in conflict with an intruding aircraft.This framework incorporates the generation of traditional aircraft trajectories using historical flight data and the Remotely Piloted Aircraft (RPA) trajectories given flight parameters in a multitude of different flight scenarios (hover, straight line, survey pattern, etc.); the addition of sensor noise to the flight tracks to emulate the expected data out of the sensors; and finally, the prediction and resolution of conflicts.Results highlight the difference in risk ratio (Probability of Near Mid-Air Collision (NMAC) with mitigation versus without mitigation) between cooperative and non-cooperative sensors.Moreover, they show the need for appropriate selection of DAA sensor parameters, e.g., field of view (FOV), range, and acceptable uncertainties, to satisfy safety requirements.
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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 | 0.004 | 0.012 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".