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Record W2890047111 · doi:10.1145/3239092.3265965

The Impact of Advanced Vehicle Technologies on Older Driver Safety

2018· article· en· W2890047111 on OpenAlexaff
Andrea D Furlan, Brenda Vrkljan, Hana H. Abbas, Jessica Babineau, Jennifer L. Campos, Shabnam Haghzare, Tara Kajaks, Maggie Tiong, Maria Vo, Martin Lavallière

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldHealth Professions
TopicOlder Adults Driving Studies
Canadian institutionsUniversity of ManitobaUniversité du Québec à ChicoutimiUniversity of TorontoMcMaster UniversityToronto Rehabilitation Institute
Fundersnot available
KeywordsWorkloadUsabilityComputer scienceApplied psychologyHuman factors and ergonomicsInclusion (mineral)Advanced driver assistance systemsOutcome (game theory)Driving simulationRisk analysis (engineering)PsychologyPoison controlHuman–computer interactionSimulationMedicineArtificial intelligenceSocial psychology

Abstract

fetched live from OpenAlex

Advanced vehicle technologies (AVTs) have the potential to modify an older driver's behind-the-wheel performance by compensating for age and/or health-related changes that can negatively impact their ability to operate a motor vehicle. However, the safety implications of these rapidly evolving technologies are not well understood. A scoping review was conducted to understand the current state of research on AVTs with a particular focus on subjective outcome measures specific to older drivers. Sixteen articles met the inclusion criteria for this scoping review. The methods used to address subjective outcomes across studies were summarized. Seven main subjective outcomes were identified: trust, functionality, satisfaction, usability, workload, acceptability, and usefulness. The results highlight inconsistencies in the research with defining these concepts. Consequently, there is an identified need for a more rigorous classification system and consistent application and interpretation of subjective measures with regard to AVTs.

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)0.001
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 score0.220
Threshold uncertainty score0.865

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.001
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 science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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 head0.026
GPT teacher head0.421
Teacher spread0.395 · 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

Citations8
Published2018
Admission routes1
Has abstractyes

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