Perception of relative distance in a driving simulator<sup>1,2</sup>
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Abstract: The aim of this experiment was to test, in a driving simulator, how a subject can control his approach towards several simulated car‐targets in different driving contexts. We assume that increasing complexity might influence driving performance according to the difficulty of perceiving distances properly. The subjects’ first task consisted of placing their car at an equal distance between two preceding cars. In the second task, the subjects had to place their car level with the preceding car. The target cars were either static or running at 40 or 60 km/h. The results showed a more precise distance perception when the difficulty of the task decreased. In all conditions the subjects underestimated distances. Subjects were better at 60 km/h than at 40 km/h and the performance improved with smaller car distances. In conclusion, the alignment tasks produced better performances than the mid‐distance tasks, as a consequence of their lower complexity. However, physical constraints due to the increase in velocity, as well as shorter distances between vehicles, improved performances.
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.021 | 0.004 |
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 it