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Record W2461203182 · doi:10.1177/0954407015605696

Rollover prevention for sport utility vehicles using a pulsed active rear-steering strategy

2015· article· en· W2461203182 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 · 2015
Typearticle
Languageen
FieldEngineering
TopicVehicle Dynamics and Control Systems
Canadian institutionsUniversity of Waterloo
Fundersnot available
KeywordsRollover (web design)CarSimAutomotive engineeringActive steeringActive safetyElectronic stability controlMATLABSimulationEngineeringSteering wheelComputer scienceVehicle dynamicsControl (management)Artificial intelligence

Abstract

fetched live from OpenAlex

Vehicle rollovers are a serious safety issue for drivers and passengers and have led to many fatal accidents on the road. This paper describes a system and a method of enhancing vehicular stability to reduce the likelihood of rollover for motor vehicles, and in particular for sport utility vehicles. Under the concept of pulsed active steering, one control strategy called pulsed active rear steering was investigated and is discussed in detail. To verify this system, the yaw and roll model was derived and the pulse signal parameters (the frequency and the amplitude) were evaluated to determine their optimum values. A full-vehicle model was built in CarSim and co-simulated with MATLAB/Simulink as the control module. Moreover, we designed and prototyped the proposed system for a sport utility vehicle and then conducted road tests with different manoeuvres. The results from simulations and experiments confirmed that the proposed pulsed active rear-steering system is promising for rollover prevention of sport utility vehicles.

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 score0.077
Threshold uncertainty score0.817

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 communication0.0000.000
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 head0.022
GPT teacher head0.234
Teacher spread0.212 · 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