MétaCan
Menu
Back to cohort
Record W1965391848 · doi:10.2991/ijcis.2011.4.6.10

Model-based simulation of driver expectation in mountainous road using various control strategies

2011· article· en· W1965391848 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 Computational Intelligence Systems · 2011
Typearticle
Languageen
FieldEngineering
TopicSimulation and Modeling Applications
Canadian institutionsMinistry of Transportation of Ontario
FundersNational Natural Science Foundation of China
KeywordsTrajectoryComputer scienceControl (management)SimulationDriving simulationVehicle dynamicsKey (lock)Driving simulatorAutomotive engineeringControl theory (sociology)EngineeringArtificial intelligenceComputer security

Abstract

fetched live from OpenAlex

Driver expectation is about driver's objective response and expectation for various traffic environments.It might directly influence the driving safety and traffic operation.This paper discusses in enough detail the shaping mechanism of driver expectation while considers road geometer parameters, driver behavior and vehicle performance in mountainous road.Combined various geometer parameters with different driving characteristics, the vehicle trajectory and speed were selected as key factors for describing completely the vehicle's operation condition.And then, the desired trajectory models were established using six basic forms of the desired trajectory.Based on the vehicle dynamics and control strategy, a desired speed model that consisted of the desired trajectory, the driver's experience and vehicle parameter was developed, the numerical simulation results show the simulation-based model of driver expectation is used to driving behavior analysis in mountainous road.

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: Simulation or modeling
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.695
Threshold uncertainty score0.556

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.000
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 head0.062
GPT teacher head0.323
Teacher spread0.261 · 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