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Record W1487938238 · doi:10.4271/2004-01-1103

Matching of Chassis and Variable Ratio Steering Characteristics to Improve High Speed Stability

2004· article· en· W1487938238 on OpenAlexaff
Andrew Heathershaw

Bibliographic record

VenueSAE technical papers on CD-ROM/SAE technical paper series · 2004
Typearticle
Languageen
FieldEngineering
TopicMechanical Engineering and Vibrations Research
Canadian institutionsBishop's University
Fundersnot available
KeywordsChassisVariable (mathematics)Matching (statistics)Automotive engineeringStability (learning theory)Computer scienceControl theory (sociology)EngineeringMathematicsArtificial intelligenceStatisticsMechanical engineeringMachine learning

Abstract

fetched live from OpenAlex

Although a vehicle with understeer is defined as a stable system, above the characteristic speed there is a reduction in yaw damping which can lead to highly oscillatory response particularly at high steering frequency inputs associated with an avoidance manoeuvre. Research was conducted to study the effect of reducing the amount of understeer to increase the characteristic speed and hence make the vehicle response less steering frequency dependent at high speed. However this also had the effect of increasing the yaw gain with regards to the steering wheel input. The yaw gain was then tuned to achieve different targets through the use of Variable Ratio (VR) steering mechanisms. This study included both a physical test of a vehicle tuned to achieve different understeer coefficients and the confirmation of the results using a vehicle model to confirm transient effects. The work indicates that by reducing the degree of understeer in conjunction with the application of a VR steering characteristic, the vehicle can be made more stable in a high speed avoidance situation.

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 machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.002
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.003
Threshold uncertainty score0.010

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.002
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)0.0030.001

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 head0.232
Teacher spread0.221 · 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 source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designBench or experimental
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

Citations7
Published2004
Admission routes1
Has abstractyes

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