Towards a Traffic Scenario Catalog for Collaborative Testing of Autonomous Vehicles
Bibliographic record
Abstract
State-of-the-art safety assurance approaches for autonomous vehicles (AVs) rely on the existence of relevant, high-quality traffic scenarios as test cases. As a key drawback, traffic scenario synthesis approaches are often expensive to compute and hard to integrate in external AV testing workflows. While different approaches make varying assumptions about the AV-under-test, they rely on similar conceptual baselines for scenario representation, which yields an opportunity for unification and collaboration within the AV testing community. In this paper, we build up on these representation similarities and propose a traffic scenario catalog to support collaborative AV testing. Our proposed model-based architecture unifies common concepts in existing AV testing approaches, thus enabling a seamless integration of their derived scenarios and test execution results. Additionally, our catalog supports abstractions over scenarios through an integrated data aggregation methodology. This allows users to empirically evaluate and compare the behavior of AV controllers, thereby identifying potential anomalies (faults), e.g., through a domain-specific adaptation of metamorphic testing.
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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 | 0.013 | 0.026 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.002 | 0.003 |
| Bibliometrics | 0.011 | 0.006 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.011 | 0.012 |
| Open science | 0.005 | 0.008 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.004 | 0.003 |
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".