Towards a Quantitative Approach for Determining DAA System Risk Ratio
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
Specific Operations Risk Assessment (SORA) is a methodology developed by the Joint Authority on Rulemaking for Unmanned Systems (JARUS) for safely conducting and evaluating Remotely Piloted Aircraft Systems (RPAS) operations in specific airspace. Many regulators, including Transport Canada (TC), the civilian aviation authority in Canada, have adopted the SORA approach to guide RPAS operators in their applications for Beyond Visual Line of Sight (BVLOS) flight. Although the qualitative approach on how to assess the performance of a Detect and Avoid (DAA) system is outlined in the SORA, a quantitative and agreed-upon approach, on how to ensure that the specific DAA system meets the required Risk Ratio criteria, has yet to be established. This paper proposes a practical approach to determining the Risk Ratio, considering sensor performance, RPA maneuvering characteristics, and airspace specifics. The developed approach relies on publicly available modelling frameworks and airspace models. Illustrative examples of applying the method to determine the Risk Ratio of specific DAA systems are presented in the paper along with a discussion on the challenges of implementing SORA into BVLOS regulations for RPAS.
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 | 0.000 | 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