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Record W2136001549 · doi:10.1109/icmech.2011.5971269

An overriding controller for vehicle lateral control system

2011· article· en· W2136001549 on OpenAlexaff
Mehran M. Shirazi, A.B. Rad, Omid Mohareri

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldEngineering
TopicAutonomous Vehicle Technology and Safety
Canadian institutionsSimon Fraser University
Fundersnot available
KeywordsRobustness (evolution)Control theory (sociology)DistractionController (irrigation)SIGNAL (programming language)Advanced driver assistance systemsControl signalControl systemComputer scienceMonte Carlo methodEngineeringControl engineeringControl (management)Artificial intelligence

Abstract

fetched live from OpenAlex

Driver assistance systems are expected to reduce the possibility of accidents caused by impaired or reckless driving. However, the current designs employed in some luxury cars are not entirely reliable and may even lead to more distraction of the driver. This paper presents the idea of an overriding controller for lateral control of a vehicle in a driver assistance system. In the proposed system, when the driver turns the steering wheel, the steer signal goes to a controller instead of the steering column of the car. The output of the controller is the actual value of steer signal which acts on the wheels. Since the driver might be in an abnormal situation while driving (due to drowsiness, fatigue, drunkenness, etc.), a second control structure is also presented in which the error signal, measured by exact sensors, is subtracted from the signal coming from the driver and resulting signal goes to the controller. Simulation results show the superiority of the proposed algorithms to the conventional driving. The stability robustness of the proposed control structures is shown using both structured singular value (μ) analysis and Monte-Carlo simulations.

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: Simulation or modeling · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.965
Threshold uncertainty score0.410

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 head0.011
GPT teacher head0.190
Teacher spread0.179 · 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 designSimulation or modeling
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

Citations3
Published2011
Admission routes1
Has abstractyes

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