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Record W2042082671 · doi:10.1115/detc2011-48036

Active Variable Wheelbase as an Innovative Approach in Vehicle Dynamic Control

2011· article· en· W2042082671 on OpenAlexaff
Avesta Goodarzi, Amir Soltani, Ebrahim Esmailzadeh

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldEngineering
TopicVehicle Dynamics and Control Systems
Canadian institutionsOntario Tech University
Fundersnot available
KeywordsAxleAutomobile handlingVehicle dynamicsAutomotive engineeringEngineeringArticulated vehicleVariable (mathematics)Electronic stability controlMoment (physics)Control theory (sociology)Control engineeringComputer scienceControl (management)TruckMechanical engineeringArtificial intelligenceMathematics

Abstract

fetched live from OpenAlex

Active variable wheelbase (AVW) has been introduced here as an innovative vehicle dynamic control method in which, the position of the front or rear axle relative to the vehicle C.G. can be actively varied. An attempt has been made to show the potential capabilities of this method in improving the road handling and stability of vehicles regardless of its embedded technical difficulties. For this purpose the proposed method has been conceptually studied in the first step and has been shown that one can generate the stabilizing yaw moment by changing the distance of the vehicle C.G. from the front or rear axles. Then the proposed concept has been theoretically studied using a simple vehicle dynamic model incorporated with the Magic Formula tire model. A comprehensive nonlinear 8 DOF vehicle model and a ‘model following control strategy’ have been used to evaluate the performance of AVW systems. The vehicle dynamic behavior when it is either uncontrolled or equipped with an AVW system has been simulated. Simulation results show that AVW can be considered as an innovative method for vehicle dynamic control in future.

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 score0.853
Threshold uncertainty score0.720

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 head0.007
GPT teacher head0.191
Teacher spread0.184 · 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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