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Record W2004010637 · doi:10.1080/00423114.2012.758857

An impaired driver model for safe driving by control of vehicle parameters

2013· article· en· W2004010637 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

VenueVehicle System Dynamics · 2013
Typearticle
Languageen
FieldEngineering
TopicAutonomous Vehicle Technology and Safety
Canadian institutionsConcordia University
Fundersnot available
KeywordsEngineeringAutomotive engineeringVehicle safetyControl (management)Vehicle dynamicsAeronauticsComputer scienceArtificial intelligence

Abstract

fetched live from OpenAlex

Abstract This paper presents the results of the investigation on a driver model that can be adjusted to perform the role of an impaired driver (especially, an alcohol-affected driver) who exhibits the deterioration in driving skills in correlation with a specific level of impairment. The linear vehicle model providing lateral displacement and yaw is coupled with the driver model that is derived as a linear quadratic regulator with delay. The decrement of performance is modelled by decreasing parameters that are preview time, visual perception, control gains and increasing reaction time. By comparing the standard deviation of the lateral position between the model and the real driver, the performance of the driver model impaired at the blood alcohol concentrations of 0.05%, 0.08% and 0.11% results in deteriorations of 21%, 26% and 30%, respectively. The lateral error is reduced if the vehicle parameters are adjusted to adapt to the impaired driver model. Keywords: vehicle modeldriver modelimpaired driverlinear quadratic regulatordelay system

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 categoriesMeta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Simulation or modeling · Consensus signal: Simulation or modeling
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.383
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 studies0.0000.000
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 head0.004
GPT teacher head0.185
Teacher spread0.180 · 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