Driver Response Time to Left-Turning Vehiclesat Traffic Signal Controlled Intersections
Bibliographic record
Abstract
<div class="section abstract"><div class="htmlview paragraph">Left-turn crashes account for almost one quarter of all collisions. Although research has quantified the response time of drivers to left-turning vehicles with high acceleration profiles, research is lacking for driver responses to realistic left-turning vehicle acceleration. The purpose of this research was to determine the Driver Response Time (DRT) to a left-turning vehicle from the first lateral movement of the left-turning vehicle. The DRT was measured from first lateral movement of the left turning vehicle, until the through driver reacts, whether by touching the brake pedal, swerving, releasing/applying the accelerator, or a combination of these inputs.</div><div class="htmlview paragraph">Ninety-eight (N<sub>Female</sub> = 48; N<sub>Male</sub> = 50) licensed volunteer drivers took part in a study at the University of Guelph Driving Research in Virtual Environments (DRiVE) lab using an Oktal complete vehicle driving simulator. After a brief practice drive to acclimatize to the virtual environment, participants completed the eight kilometer drive experiment where two types of left hand turn hazards were presented to drivers in random order. In one scenario, the left-turning vehicle was stopped at the intersection before accelerating (LHTS), and in the other scenario the vehicle approached the intersection and turned at a constant speed (LHTNS). There were significant differences in DRT values between the LHTS and LHTNS scenarios with drivers taking longer to respond to the LHTS vehicle. This difference in DRT values corresponded with a higher collision rate in the LHTS scenario when compared to the LHTNS. However, collisions were common in both situations, with a mean time-to-impact of 3.66 seconds. Female DRT trended toward being slightly longer than males, but there were no differences in collision rates.</div></div>
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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.002 |
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; both teacher heads agree on what is shown here.
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".