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Record W2137352707 · doi:10.1504/ijvics.2005.007589

An HSGPS, inertial and map-matching integrated portable vehicular navigation system for uninterrupted real-time vehicular navigation

2005· article· en· W2137352707 on OpenAlexaff
Chaminda Basnayake, O. Mezentsev, Gérard Lachapelle, M. Elizabeth Cannon

Bibliographic record

VenueInternational Journal of Vehicle Information and Communication Systems · 2005
Typearticle
Languageen
FieldEngineering
TopicIndoor and Outdoor Localization Technologies
Canadian institutionsUniversity of Calgary
Fundersnot available
KeywordsGlobal Positioning SystemMap matchingInertial measurement unitInertial navigation systemComputer scienceReal-time computingNavigation systemDead reckoningMatching (statistics)Inertial frame of referenceArtificial intelligenceTelecommunications

Abstract

fetched live from OpenAlex

Robust real-time vehicle positioning is a critical requirement for many in-vehicle technologies. However, no standalone positioning system is capable of providing uninterrupted and accurate vehicular navigation data in all environments that a vehicle could encounter. This paper presents a portable vehicular navigation system that combines high-sensitivity GPS, inertial sensors and map-matching techniques to provide uninterrupted vehicular navigation information in even the harshest environments for individual sensor technologies. This system eliminates the need for complex initialisation routines required for high-end inertial sensors, making it a truly portable system. The map-matching component establishes a link between the vehicle and spatial information useful for many more in-vehicle technologies such as route guidance and location-based services. The system performance is evaluated and presented in an urban centre with extreme levels of GPS signal degradation.

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.

How this classification was reachedexpand

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 score0.161
Threshold uncertainty score0.670

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 communication0.0000.003
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 head0.237
Teacher spread0.231 · 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

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designSimulation or modeling
Domainnot available
GenreEmpirical

How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".

Quick stats

Citations20
Published2005
Admission routes1
Has abstractyes

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