Driver Response to Right Turning Path Intrusions at Signalized Intersections
Bibliographic record
Abstract
<div class="section abstract"><div class="htmlview paragraph">Previously researched path intrusion scenarios include left-turning hazard vehicles which intrude laterally across the path of the through driver. A right turning vehicle, however, creates a scenario where a hazard which was initially travelling perpendicular to the driver can intrude into the through driver’s path without also occupying the adjacent through lanes. This hazard scenario has not been previously investigated. The purpose of this research was to determine driver response time (DRT) and response choice to a right turning vehicle that merges abruptly into the lane of the oncoming through driver.</div><div class="htmlview paragraph">Using an Oktal full car driving simulator, 107 licenced drivers (N<sub>Female</sub> = 57, N<sub>Male</sub> = 50) completed a five-minute practice drive followed by a ten-minute experimental drive containing two conditions of the right turn hazard, presented in a counterbalanced order. In one condition, the hazard vehicle was stopped with its front bumper at the stop bar before accelerating into the path of the participant driver. In the other condition, the hazard vehicle approached the intersection and turned at a constant speed. DRT was defined as the time between when the hazard vehicle crossed the stop bar and when the participant driver reacted by touching the brake pedal or swerving.</div><div class="htmlview paragraph">There was a significant difference in DRT (p &lt; 0.001) between the two hazard conditions with drivers responding earlier to the right turning vehicle when it was initially in motion. In both scenarios, approximately half of the through drivers swerved in response to the hazard vehicle. Participants who chose to swerve were slower on average, although this response type did not result in significantly more collisions (χ<sup>2</sup>(2) = 2.02, p = 0.14).</div></div>
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 | 0.001 |
| 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 | 0.001 |
| 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".