Driver Response to Left Incurring Path Intrusions at Sign-Controlled Intersections
Bibliographic record
Abstract
Straight intersecting path or “side” collisions account for 12% of all motor vehicle crashes and 24% of fatalities. While previous research has examined driver responses to hazards striking from the right (near side), no research has quantified driver responses to hazards striking from the left (far side) of an intersection. The purpose of this study was to measure driver response time (DRT) and response choice for two versions of this scenario. In one condition, the hazard vehicle was initially stopped at the intersection before accelerating into the path of the participant driver. In the other condition, the hazard vehicle approached and entered the intersection while moving at a constant speed of 50 km/h. Testing was conducted using an Oktal full car driving simulator. 107 licenced drivers (NFemale = 57, NMale = 50) completed a short familiarization drive followed by the experimental drive in which they encountered both the initially stopped and moving conditions of the straight path hazard, in a counterbalanced order. DRT was defined as the time between when the hazard vehicle crossed a trigger located two meters from the curb until the driver reacted either by touching the brake pedal or swerving. The average DRT was 0.08 seconds in the initially moving hazard condition and 2.39 seconds in the stopped condition. In the moving condition, many participants were able to anticipate the hazard before it crossed the defined starting location. To account for the more obvious hazard precursor in the moving condition, a geographic location further from the intersection is proposed as the starting location for measuring DRT in gradually developing hazard scenarios. There was also a significant difference in DRT (F (2) = 5.55, p = 0.005) based on the avoidance action taken. Drivers who braked and steered had the longest response times and they were also more likely to get into a collision.
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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) | 0.003 | 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".