Driver Response Time to Left-Turning Vehiclesat Traffic Signal Controlled Intersections
Bibliographic record
Abstract
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. Ninety-eight (NFemale = 48; NMale = 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.
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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 | 0.004 |
| 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) | 0.007 | 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".