Measuring safety benefits of a connected cruise control–equipped vehicle in a connected road environment
Bibliographic record
Abstract
This study examines the safety impacts of introducing a connected cruise control–equipped vehicle with a collision warning system using a driving simulator. The collision warning system serves to alert drivers to downstream collisions. Various scenarios were designed, taking into account factors like message content, delivery method, and vehicle-to-vehicle connectivity range. To determine the effects of connected cruise control, surrogate safety indicators, such as speed and time headway variabilities, minimum time to collision, and maximum braking, were examined. Additionally, the study identified tailgaters, individuals prone to rear-end collisions, using a newly developed rear-end accident risk index. Further, the association of driver characteristics with their car-following behaviour was tested using ANOVA. The findings revealed that in the connected environment, safety metrics exhibited reduced mean values compared to the non-connected environment. The risk index effectively detected tailgating behaviour when specific thresholds were applied. Moreover, interesting findings were observed in the ANOVA analysis.
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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 | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| 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".