Adaptation Longitudinal Driving Behavior, Mental Workload, and Psycho-Spacing Models in Fog
Bibliographic record
Abstract
Adverse weather conditions have a substantial effect on traffic flow. However, the adaptation effects in longitudinal driving behavior that underlie this impact are unclear, as are the determinants. A driving simulator experiment was performed with a repeated-measures design and 25 participants. The adaptation effects in actual longitudinal driving behavior and the physiological indicators of mental workload (i.e., heart rate and heart rate variability) were measured under two conditions: normal visibility and fog. Significant adaptation effects in longitudinal driving behavior and a significant increase in mental workload were observed. A new estimation method was used to investigate the extent to which fog influenced the position of so-called action points in the (Δv, s) plane of a psycho-spacing model, where Δv was relative speed and s was spacing. In addition, multivariate regression analysis was applied to investigate the extent to which an influence could be observed on acceleration and on jumps in acceleration at the action points. Large differences in the positions of action points in the (Δv, s) plane, acceleration, and jumps in acceleration were observed between conditions; therefore, car-following patterns closely resemble those predicted by psycho-spacing theory. However, a large degree of inter- and intradriver heterogeneity was observed, possibly caused by differences in mental workload within and between drivers. This heterogeneity indicates that the assumption of deterministic perceptual thresholds is unrealistic and necessitates the development of a data-driven stochastic model based on the principles of psycho-spacing models.
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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 | 0.002 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".