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Record W2137878230 · doi:10.1518/0018720054679443

Perceptual Processes Used by Drivers During Overtaking in a Driving Simulator

2005· article· en· W2137878230 on OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.

Bibliographic record

VenueHuman Factors The Journal of the Human Factors and Ergonomics Society · 2005
Typearticle
Languageen
FieldPsychology
TopicHuman-Automation Interaction and Safety
Canadian institutionsYork University
Fundersnot available
KeywordsOvertakingDriving simulatorSimulationAdaptation (eye)PerceptionPoison controlControl (management)Closing (real estate)Computer scienceAeronauticsEngineeringTransport engineeringPsychologyArtificial intelligence

Abstract

fetched live from OpenAlex

This study investigated the control strategies and decision making of drivers who were executing overtaking maneuvers in a fixed-base driving simulator. It was found that drivers were frequently inaccurate in deciding whether it was safe to overtake in front of an oncoming vehicle. One source of error in this situation was the control strategy adopted by the driver; in several instances our drivers initiated an overtaking maneuver when the oncoming car's distance was above a critical value, even though there was not sufficient time to complete a safe maneuver. Adaptation to closing speed (produced by driving on a straight open road) also had large effects on overtaking behavior. For all participants, closing speed adaptation resulted in decisions that were delayed, of higher risk, and more variable. Actual or potential applications of this research include improved training for younger drivers and the development of in-car interfaces that reduce closing speed adaptation.

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.

Full frame distilled prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesInsufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.263
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0010.000
Scholarly communication0.0000.000
Open science0.0010.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0010.000

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.

Opus teacher head0.028
GPT teacher head0.308
Teacher spread0.281 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it