Driver Response Time to Cyclist Path Intrusions
Bibliographic record
Abstract
Motor vehicle crashes with cyclists are on the rise, with a six percent increase in fatal crashes from 2006 to 2015 in the USA. Although some research exists on the response time of drivers to some types of path intrusions, data on the perception-response of through drivers to cyclists who fail to stop at a stop sign, and ride into the path of the vehicle has not been researched. The purpose of this study was to quantify the Driver Response Time (DRT) to a cyclist that intrudes perpendicularly in front of a through vehicle at an intersection where the driver has the right-of-way. The DRT was measured from when the cyclist is positioned at the stop sign until the driver reacts, whether by touching the brake pedal, swerving (steering wheel angle change of at least 2 degrees), accelerating, or a combination of those responses. 26 (NFemale = 13; NMale = 13) university aged licensed volunteer drivers participated in the study conducted 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 approximately 10 minute experiment drive during which the cyclist hazard was presented. About one quarter of drivers crashed into the cyclist, with a mean time-to-impact of 3.26 seconds. There were no gender differences in terms of DRT or 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 | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.006 | 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".