Bibliographic record
Abstract
The ability to anticipate future events in the traffic environment is an important competence in driving. This paper extends our prior work: 1) to show potential benefits resulting from anticipatory competence in driving, and 2) to collate characteristics of anticipatory competence from a theoretical point of view. The reviewed literature is foundational to our understanding of anticipation as a high-level cognitive competence, allowing for the prediction of future traffic situations on a tactical level. We conceptualize anticipation as relying on the identification of stereotypical traffic situations based on indicative cues, and stress that the impacts of this competence are dependent on the driver's individual goals. Thus, anticipation enables a number of potential benefits, such as safety and fuel-efficiency, but the realization of these potential benefits depends on the goals of the driver. Further, we argue that the superior anticipatory competence of experienced drivers observed in an earlier simulator study can be explained via their heightened ability both to identify indicative cues, and interpret those cues relative to similar, memorized situations. We then capture anticipatory driving in a model inspired by the classical theory of information processing to describe the various steps necessary to process indicative cues from the environment, anticipate a future traffic situation, and take appropriate action or achieve a state of cognitive readiness.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.013 | 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".