Preliminary Definition of Detection and Reaction Boundaries for Autonomous Marine Traffic
Bibliographic record
Abstract
The goal of this project is to outline a preliminary approach to quantitatively define surveillance, declaration, and operating boundaries for autonomous marine traffic. This will be accomplished through an adaptation of Detect and Avoid Alerting Logic for Unmanned Aircraft (DAIDALUS) to the marine environment. Using the manoeuvring characteristics of a known vessel to manoeuvre, encounter geometries are analyzed using separation boundaries for safe operation and remedial action is suggested when a violation is expected to occur. Within this paper's scope, a formal definition of an autonomous vessel's alerting behaviour for various potential collision geometries is provided and applied to the development of collision avoidance scenarios. This definition, combined with the application in DAIDALUS, will provide the framework for defining the regulatory boundaries. Preliminary results are analyzed to inform regulatory development, refine the control approach to improve the safe, reliable operation of autonomous marine vessels and to investigate the feasibility of the selected method.
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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 | 0.000 | 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 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".