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Record W2945856812 · doi:10.1049/iet-rsn.2019.0004

Adaptive cruise control radar‐based positioning in GNSS challenging environment

2019· article· en· W2945856812 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

VenueIET Radar Sonar & Navigation · 2019
Typearticle
Languageen
FieldEngineering
TopicGNSS positioning and interference
Canadian institutionsRoyal Military College of CanadaQueen's University
Fundersnot available
KeywordsGNSS applicationsCruise controlRadarCruise missileComputer scienceCruiseRemote sensingEnvironmental scienceGeographyGlobal Positioning SystemControl (management)TelecommunicationsArtificial intelligenceEngineeringAerospace engineering

Abstract

fetched live from OpenAlex

Autonomous and land vehicles’ navigation in urban canyons requires aiding from other systems to the Global Navigation Satellite System (GNSS). This kind of environment is characterised by containing high rise buildings and long tunnels which interfere with the GPS satellite's signals causing its partial or total blockage. Therefore, the utilisation of another positioning source with high fidelity solution is essential during long outage periods. The Adaptive Cruise Control (ACC) system is a critical unit in the Advanced Drive Assistant System. The ACC measures the relative speed and distance between the on‐board vehicle and the vehicle in front. In this study, the ACC radar and an azimuth gyroscope are utilised to produce a self‐contained positioning system. The position solution of this system is utilised to update the Inertial Navigation System during the GNSS outage periods. The proposed system was tested over real road trajectories which were conducted in an urban canyon to validate the efficiency of the system.

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: Empirical
Teacher disagreement score0.089
Threshold uncertainty score0.898

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 head0.007
GPT teacher head0.188
Teacher spread0.182 · 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