Theoretical Model Of The Visibility Level And Practical Means Of Its Implementation
Bibliographic record
Abstract
Driving a car, especially in city traffi c, is a greatly complex process combining observation, recognition and psychomotoric functions.Safe, effi cient and comfortable driving requires a specifi c level of visibility of road obstacles.The diffi culty in spotting an obstacle in the road and in evaluating its effect on driving depends on such factors as lighting conditions in the road and its vicinity, presence of sources of glare, sources of distracting and attracting attention in the driver's fi eld of vision, for example, electronic outdoor advertising boards (LED billboards), the obstacle's geometric and photometric properties, observation conditions and the driver's visual performance.The research on the visibility of obstacles in the road has shown that the satisfaction of normative requirements in relation to average luminance and the general and longitudinal uniformity does not guarantee that an obstacle will be spotted.Thus, it is necessary to introduce another criterion to make it possible to evaluate the visibility of obstacles in the road.Visibility formula was described by Adrian in 1989 and applied with visibility levels in North America as quality criterion.For the purposes of designing road lighting systems, the visibility criterion is not used in European countries yet.Due to simplifi cations, other standards and requirements, it is also impossible to directly employ the visibility criterion used in United States, namely the Small Target Visibility, based to a large extent on Adrian's visibility model.
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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 | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.003 | 0.004 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.007 | 0.002 |
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".