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Record W1991787316 · doi:10.1115/1.4006047

An Automated Design Method for Active Trailer Steering Systems of Articulated Heavy Vehicles

2012· article· en· W1991787316 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

VenueJournal of Mechanical Design · 2012
Typearticle
Languageen
FieldEngineering
TopicVehicle Dynamics and Control Systems
Canadian institutionsOntario Tech University
FundersUniversity of Michigan
KeywordsControl engineeringEngineeringTrailerAutomationStability (learning theory)Controller (irrigation)Bilevel optimizationVariable (mathematics)Design methodsComputer scienceControl theory (sociology)SimulationAutomotive engineeringControl (management)Optimization problemArtificial intelligence

Abstract

fetched live from OpenAlex

An important design decision for active trailer steering (ATS) systems for articulated heavy vehicles (AHVs) is the trade-off between maneuverability and lateral stability. This paper presents an automated design method for this trade-off. The proposed method has the following features: (1) a design framework for bilevel optimization of ATS systems is formulated; (2) design variables of ATS controllers and trailers are optimized simultaneously; (3) two controllers are designed for the ATS system for improving stability and enhancing maneuverability, respectively; and (4) a driver model is introduced in the virtual vehicle simulation for closed-loop testing maneuvers. The design framework allows automation of vehicle modeling, controller construction, performance evaluation, and design variable selection, and all required design processes are implemented in a single loop. The proposed method is compared to a previously published two-loop design method, showing that the new approach can effectively identify desired variables and predict performance envelopes.

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)0.003
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: Methods · Consensus signal: none
Teacher disagreement score0.879
Threshold uncertainty score0.674

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0030.000
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 head0.031
GPT teacher head0.285
Teacher spread0.254 · 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