Towards Automated Test Scenario Generation for Assuring COLREGs Compliance of Autonomous Surface Vehicles
Bibliographic record
Abstract
International maritime traffic is controlled by collision-avoidance regulations (COLREGs) with 41 standardized rules describing how a vessel should navigate in the proximity of other vessels. Since some rules can be overridden by human judgement when resolving critical encounters of vessels, justifying COLREGs compliance has become a significant challenge in the increasing presence of autonomous surface vehicles (ASVs) operated without (or with only remote) human control. This paper provides a high-level framework and long-term research agenda towards the automated synthesis of test scenarios to assure COLREGs compliance for ASVs by exploiting various model-driven engineering techniques. By adapting ideas from testing self-driving cars, we envisage a multi-layered test scenario generation approach involving functional, logical and concrete scenarios. In the current paper, we demonstrate how functional scenarios of COLREGs situations between given vessels can be precisely formalized by using metamodels, domain-specific graph models and first-order logic graph constraints. By using automated model generation techniques, we derive a complete set of functional-level test scenarios, which includes all possible COLREGs situations that may arise between given vessels. As initial result, we provide several dangerous situations involving only three vessels where a potential collision may occur even when all vessels follow the COLREGs, which showcases that some COLREGs rules need further clarification for the safe regulation of ASVs.
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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 | 0.003 | 0.014 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.002 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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 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".