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Record W2140978786 · doi:10.1177/0954407012454101

An optimal preview driver model applied to a non-linear vehicle and an impaired driver

2012· article· en· W2140978786 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

VenueProceedings of the Institution of Mechanical Engineers Part D Journal of Automobile Engineering · 2012
Typearticle
Languageen
FieldEngineering
TopicVehicle Dynamics and Control Systems
Canadian institutionsConcordia University
Fundersnot available
KeywordsAdvanced driver assistance systemsPath (computing)Computer scienceDriving simulatorSimulationCar modelAutomotive engineeringLinear modelControl theory (sociology)Control (management)EngineeringArtificial intelligence

Abstract

fetched live from OpenAlex

This paper proposes an improved driver model based on the one introduced by MacAdam in 1981 where optimal preview control is applied. The details of the optimal preview driver model are derived. The modified model incorporating the steering angle into the performance index is used to compute the steering input. The parameters associated with reaction time and preview time characterize the adaptation of the driver to the changes of the vehicle, the road and the environment. The modified model exhibits better performance and its parameters are well related to the performance of the driver. The driver model is then coupled with a non-linear vehicle. Initial tests are performed to identify the driver’s parameters. The coupling of the driver and the non-linear vehicle introduces enhanced path-following. An impaired driver model can be obtained by reducing the optimal parameters. When the safety criteria are chosen, the threshold for safe driving is identified.

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)0.001
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: Simulation or modeling · Consensus signal: Simulation or modeling
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.198
Threshold uncertainty score0.847

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.001
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 head0.008
GPT teacher head0.217
Teacher spread0.209 · 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