Impact of Cognitive Distractions on Drivers’ Anticipation Behavior in Vehicle-bicycle Conflict Situations
Bibliographic record
Abstract
Overall, the rate of vehicle-bicycle collisions is continually increasing. In the United States alone, bicyclist fatalities contributed to 2.3 percent of all crash related fatalities in 2015. In most of these cases, crashes occur due to distracted drivers who are unable to correctly anticipate the bicyclists at the hazardous locations on the roadways such as, intersections and curves. The objective of the current study is to contribute to the divisive literature surrounding cell phone use while driving by specifically measuring, the effects of a secondary mock cell phone task on hazard anticipation performance across common vehicle-bicycle conflict situations. Two groups of 20 drivers each, navigated seven unique scenarios on a driving simulator while being monitored by an eye tracker. One group of participants performed a hands free mock cellphone task while driving, while the second group drove without any additional tasks outside of the primary task of driving. Analysis of the proportion of anticipatory glances using a logistic regression model revealed a significant main effect of the mock cellphone task at reducing the proportion of such glances made by the drivers towards potential bicyclist threats on the roadway.
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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 | 0.000 | 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".