The Effect of In-Vehicle Advanced Signs on Older and Younger Drivers’ Intersection Performance
Bibliographic record
Abstract
An experimental study was conducted to determine if intersection behavior of those 18 to 24 and 65+ benefited from advanced in-vehicle signs presented in a head-up display (HUD) format. Using the University of Calgary Driving Simulator (UCDS) to measure intersection performance in the presence of the advanced sign until drivers stopped or cleared the intersection. Two in-vehicle signs, presented in a head-up display format, were evaluated to determine if intersection performance improved or whether unwanted adaptive behaviors occurred. In-vehicle signs facilitated more younger and older drivers to come to a stop at intersections with relatively short yellow onsets. In addition, the speed of those who stopped and those who proceeded through the intersection was reduced by the in-vehicle signs. The velocity reduction produced by the in-vehicle signs was greatest at yellow onset and progressively less effective at stop-line and intersection exit measurement locations. The primary behavioral influence of the in-vehicle signs was on removing the drivers foot from the accelerator in advance of the light changes. Older drivers had slower intersection approach speeds, stopped more accurately and were more likely to not clear the intersection before the traffic light turned to all red, than younger drivers.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
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 | 0.000 | 0.000 |
| 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) | 0.000 | 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 teacher head, 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".