Unifying expert knowledge and field data toward an enhanced scenario description for CAV certification: a comprehensive scenario-based approach
Bibliographic record
Abstract
With the emergence of studies on automated vehicles, the rapid development of new systems raises questions about road safety certification. Numerous methods have been developed to address this, such as Distance-Based and Scenario-Based approaches. The latter offers a time-saving advantage by avoiding redundant testing and focusing on traffic situations that pose risks to the system. However, scenarios can vary in levels of abstraction depending on the system’s design. Many studies attempt to identify safety criteria using indicators based on real-world scenarios, but perform at a low level of abstraction for the scenario description. Only a little draws analysis at the primary, i.e., abstract, level. Consequently, the qualification of abstract scenarios concerning safety indicators remains difficult and extremely dependent on field observations without harmonization. No link is clearly established yet between the abstract description of the experienced situation and indicators resulting from field observations. In a generic sense, abstract levels are managed at the expert level. This study establishes a rare connection between concrete (low abstraction level) and functional (high abstraction level) scenarios to compute the criticality of traffic scenarios using UAV data. Our approach develops the methodology to unify expert knowledge (top-down approach) with field observations (bottom-up approach).
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.007 | 0.014 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.009 | 0.004 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.005 | 0.006 |
| Open science | 0.003 | 0.005 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".