DAAMSIM: A simulation framework for establishing detect and avoid SYSTEM requirements
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Performance requirements for detect, alert, and avoid (DAA) systems for remotely piloted aircraft systems (RPAS) are under development by many regulatory agencies and standards bodies. A DAA system can be decomposed into three functions, “detect” — situational awareness; “alert” — determination of traffic that may be in conflict, evaluation of the de-conflicting flight path, and informing the pilot-in-command; and “avoid” — avoidance maneuver execution, and determination of “clear of conflict”. The “Detect” function of a DAA system depends on the sensor, target, and environment characteristics (e.g., signal-to-noise ratio of the target vs. background). The “alert” function depends on conflict prediction algorithms and human factors requirements. The “avoid” function depends on the RPAS maneuvering performance, airspace “rules”, and the size of the protection volume. The aforementioned factors impact the time required to calculate, and execute, an avoidance maneuver that will guarantee a prescribed miss distance, and dominate the “detect” requirements of a sensor. This paper describes DAAMSim: a publicly available modeling and simulation framework, developed by the National Research Council of Canada, to support the determination of DAA system requirements, and evaluation of DAA system performance. The framework described herein incorporates the functional components including various sensor, tracker, and avoid models; data replay; visualization tools; and offline metrics. Further, this paper presents sample results of the framework’s ability to determine DAA system requirements for various degrees of RPAS and intruder performance, and concludes with a description of future work activities.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 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) | 0.000 | 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 it