A Contribution to Situation Awareness Analysis: Understanding how mismatched expectations affect road safety
Bibliographic record
Abstract
The aim of the study was to clarify how knowledge elaborated by specific experience may lead to erroneous expectations during interactions between drivers and riders. Situation Awareness is partly determined by prior knowledge. Unshared knowledge may cause difficulties in managing driving interactions, but there is still an important gap in the literature devoted to this field of research. 226 participants were distinguished according to their use (type of vehicle driven, exclusive or dual drivers; and for the riders, the type of power two-wheeler used and its engine size) and their driving experience. Focusing on the most vulnerable users, prior representations to interactions were studied using a series of closed questions on drivers' performances relating to different stages of the interaction process from both the perspective of drivers' self-reflection and of riders' expectation. While most drivers are self-confident, their abilities tend to be questioned by riders. Owners of medium or large motorbike feel that drivers do not assess their approach speed accurately. Similarly, scooter riders doubt their ability to assess the distance that separates them from PTWs. Riders who use medium or large motorbikes are more likely to question drivers' skills in relation to crossing situations. Scooter riders do so more often for overtaking situations. The development of shared prior knowledge is essential to prevent accidents and incidents between drivers and riders. To help improve effectiveness, we recommend specific ways of embedding each type of road user profile in training, prevention and research.
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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 | 0.003 | 0.022 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.004 | 0.005 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".