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Record W2401981049 · doi:10.1504/ijvp.2015.074123

Design validation of active trailer steering systems for improving the low-speed manoeuvrability of multi-trailer articulated heavy vehicles using driver-hardware/software-in-the-loop real-time simulations

2015· article· en· W2401981049 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

VenueInternational Journal of Vehicle Performance · 2015
Typearticle
Languageen
FieldEngineering
TopicVehicle Dynamics and Control Systems
Canadian institutionsOntario Tech University
Fundersnot available
KeywordsTrailerHardware-in-the-loop simulationAutomotive engineeringSoftwareComputer scienceSimulationCar drivingEngineeringEmbedded system

Abstract

fetched live from OpenAlex

This paper presents a design validation method for active trailer steering (ATS) for improving low-speed manoeuvrability of multi-trailer articulated heavy vehicles (MTAHVs) using driver-hardware/software-in-the-loop (DH/SIL) simulations. A numerical-optimisation-based design of a MTAHV with ATS was reported. The ATS is featured with two controllers, one for improving the manoeuvrability and the other for enhancing the stability. To validate the design for minimising the path-following off-tracking (PFOT), real-time simulations are conducted on a DH/SIL platform fabricated by combining a vehicle simulator with two physical ATS axles. The PFOT controller is reconstructed and integrated with the real-time MTAHV model for DH/SIL simulations. A benchmark is performed for three designs: 1) without the PFOT controller and the physical ATS axles; 2) only with the PFOT controller; 3) with both the PFOT controller and the ATS axles. The benchmark validates the reported design and demonstrates the effectiveness of the proposed design validation method.

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 score0.140
Threshold uncertainty score0.457

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.001
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 head0.034
GPT teacher head0.263
Teacher spread0.229 · 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