Influences of Waiting Time on Driver Behaviors While Implementing In-Vehicle Traffic Light for Priority-Controlled Unsignalized Intersections
Bibliographic record
Abstract
In-vehicle traffic lights that assist drivers in crossing intersections are in development; however, the availability of the in-vehicle traffic light will be limited if the waiting time of a vehicle is not considered in actual traffic conditions, especially at priority-controlled unsignalized intersections that normally consist of one major and two minor roads. The present study therefore investigated the effects of the waiting time on driver behaviors to improve the in-vehicle traffic light for the priority-controlled unsignalized intersections. Gap acceptance theory that considers the waiting time was adopted in the implementation of the in-vehicle traffic light, to assist minor-road drivers in passing through the intersections by selecting appropriate major-road gaps. A driving simulator experiment involving 12 participants was performed for the minor and major roads, by applying the in-vehicle traffic light with and without the consideration of waiting time. Results demonstrate that the maximum acceleration strokes of minor-road vehicles were significantly reduced, indicating a lower possibility of aggressive driving when the in-vehicle traffic light was applied while considering the waiting time. Meanwhile, an improved steering stability was observed from the driver behaviors at the intersections, as the maximum lateral acceleration of minor-road vehicles significantly decreased when the waiting time was considered.
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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 | 0.001 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".