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Record W175365921

In-Vehicle Intelligent Transportation System (ITS) Countermeasures to Improve Older Driver Intersection Performance

2006· article· en· W175365921 on OpenAlexaboutno aff
J K Caird, Sl Chisholm, Julie Lockhart, Natalie H Vacha, C.J. Edwards, Ji Creaser, K Hatch

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldHealth Professions
TopicOlder Adults Driving Studies
Canadian institutionsnot available
Fundersnot available
KeywordsIntersection (aeronautics)ClearanceAdvanced driver assistance systemsIntelligent transportation systemTransport engineeringDriving simulatorComputer scienceMeasure (data warehouse)SimulationEngineeringArtificial intelligenceMedicineData mining
DOInot available

Abstract

fetched live from OpenAlex

A literature review of in-vehicle intelligent transportation systems (ITS) is presented for the purposes of determining which technologies may benefit older drivers at intersections. Based on the review, three experimental studies were conducted to determine intersection behaviour of those aged 18 to 24, 25 to 35, 55 to 64 and 65+ with the assistance of a range of in-vehicle intersection signs. The first experiment used the University of Calgary Driving Simulator (UCDS) to measure intersection performance at amber onset until drivers stopped or cleared the intersection. In the second experiment, two in-vehicle signs, presented in a head-up display format, were evaluated to determine if intersection performance improved or unwanted adaptive behaviours occurred. The third experiment tested the comprehension of 24 in-vehicle signs by older and younger drivers. Overall, the multi-method, multi-measure approach describes older driver performance at intersections, with an innovative in-vehicle technology and design guidelines for that technology.

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 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 categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.025
Threshold uncertainty score0.995

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 studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.001

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.

Opus teacher head0.018
GPT teacher head0.323
Teacher spread0.305 · 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

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreEmpirical

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".

Quick stats

Citations9
Published2006
Admission routes1
Has abstractyes

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