A Ship Domain-Based Method of Determining Action Distances for Evasive Manoeuvres in Stand-On Situations
Bibliographic record
Abstract
A ship encounter can be considered safe if neither of ships’ domains (defined areas around ships) is intruded by other ships. Published research on this includes optimising collision avoidance manoeuvres fulfilling domain-based safety conditions. However, until recently there was no method, using ship’s domain to determine exact moment when a particular collision avoidance manoeuvre can still be successfully performed. The authors have already proposed such method for give-way encounters. In the paper, documenting continuation of the research, another kind of scenarios is considered. This paper is focused on situations where the own ship is the stand-on one and the target is supposed to manoeuvre. The presented method uses a ship’s dynamics model to compute distance necessary for a manoeuvre successful in terms of avoiding domain violations. Additionally, stability-related phenomena and their impact on possible manoeuvres in heavy weather are taken into account. The method and applied models are illustrated in a series of simulation results. The simulations cover various examples of stand-on situations, including encounters in heavy weather conditions. Discussed manoeuvres may be limited to course alteration or may combine turns with speed reduction.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 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) | 0.003 | 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".