Assessing Scene Generation Techniques for Testing COLREGS-Compliance of Autonomous Surface Vehicles
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
Autonomous surface vehicles (ASVs) need to complete missions without posing risks to other maritime traffic. Safe traffic in open sea encounters is controlled by the International Regulations for Preventing Collisions at Sea (COLREGS) formulated by the International Maritime Organization (IMO). Designed with human operators in mind, the COLREGS are intentionally underspecified, which may result in ambiguous requirements for correct behaviour for ASVs. Hence the systematic testing of such ambiguous situations is particularly important. This paper investigates to what extent existing test scenario generation approaches deemed effective in the automotive domain can be adapted to test COLREGS-compliance in a maritime context with multi-vessel encounters. In a series of experiments involving synthetic and real-world test scenarios, their performance is evaluated with respect to relevance, diversity, completeness, scalability and speed. Our results indicate that (1) test scenarios derived from historic maritime traffic are insufficient for testing multi-ship encounters. Moreover, (2) existing test scenario generation techniques provide sufficient scalability and speed, but they are very limited in terms of diversity and completeness when the number of vessels increases.
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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 | 0.002 |
| 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 | 0.001 | 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