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Record W3127971122 · doi:10.1109/cac51589.2020.9326596

Post-Impact Stability Control for Four-Wheel- Independently-Actuated Electric Vehicles

2020· article· en· W3127971122 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

Venuenot available
Typearticle
Languageen
FieldEngineering
TopicVehicle Dynamics and Control Systems
Canadian institutionsUniversity of Waterloo
FundersNovaChina Scholarship CouncilMinistry of Science and Technology
KeywordsCarSimControl theory (sociology)TorqueVehicle dynamicsImpulse (physics)Electronic stability controlMATLABAutomotive engineeringComputer scienceYawElectric vehicleController (irrigation)Moment (physics)EngineeringControl (management)

Abstract

fetched live from OpenAlex

Relevant studies show that vehicle instability such as drifting and spinning after the first impact may have further severe implications in road vehicle collision accidents. This paper presents a post-impact stability control scheme for four-wheel-independently-actuated electric vehicles (FWIA EVs). First, a sliding mode controller is designed to produce the reference yaw moment to attenuate undesired yaw motion after the first impact. Then, an optimization-based algorithm is developed for optimal wheel torque allocation and steering angle coordination to follow the derived reference yaw moment. Finally, the holistic algorithm is verified through co-simulation of Matlab/Simulink and CarSim. The verification results show that the developed scheme performs well and can maintain the stability of the test vehicle after a maximum lateral-rear impact impulse of 3500 Ns.

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)0.000
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 score0.955
Threshold uncertainty score0.816

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 head0.014
GPT teacher head0.208
Teacher spread0.194 · 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

Quick stats

Citations7
Published2020
Admission routes1
Has abstractyes

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