Identifying potential tailgaters using matched case-control logistic regression
Bibliographic record
Abstract
Road collisions arise from interactions involving human factors, the environment, and road layout. Driving simulators, widely applied in rear-end collision studies, provide a secure environment to explore human errors, which are not observable through microsimulation tools. These simulators also facilitate the examination of driving behaviour in the presence of connected vehicles. This study aims to identify driver-related factors contributing to rear-end collisions in a driving simulator and to detect potential tailgaters behind a connected vehicle with connected cruise control. Using case-control logistic regression, participants with the potential to be involved in rear-end collisions are considered potential tailgaters, while non-potential participants serve as controls. The results reveal statistically significant factors, such as headway time and maximum brake mean values, in relation to rear-end collisions. Furthermore, employing regression outputs, log relative risk, and survival function, with predefined thresholds effectively identifies potential tailgaters, achieving accuracy rates of over 90% and 97%, respectively.
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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 | 0.015 | 0.047 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".