Anticipation in Driving: The Role of Experience in the Efficacy of Pre-event Conflict Cues
Bibliographic record
Abstract
Anticipation of future events is recognized to be a significant element of driver competence. Surely, guiding one's behavior through the anticipation of future traffic states provides potential gains in recognition and reaction times. However, the role of anticipation in driving has not been systematically studied. In this paper, we identify the characteristics of anticipation in driving and provide a working definition. In particular, we distinguish it from driving goals such as eco or defensive driving and define it as a high-level competence for efficient positioning of the vehicle to facilitate these goals. We also present a driving simulator study assessing the relation between driver experience and anticipation. Thirty drivers from three different experience categories (low, medium, and high) completed five scenarios, each involving several pre-event cues designed to allow the anticipation of an event. The results showed that more experienced drivers demonstrated more pre-event actions compared with less experienced drivers. While pre-event actions resulted in improved safety on certain occasions, the effects were often not significant. Future research should further investigate the mechanisms underlying anticipation, particularly how drivers make use of temporal and spatial gains obtained through the recognition of pre-event cues.
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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 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".