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Record W4232104377 · doi:10.1504/ijehv.2019.102877

A review of autonomous vehicle technology landscape

2019· review· en· W4232104377 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 Electric and Hybrid Vehicles · 2019
Typereview
Languageen
FieldEngineering
TopicAutonomous Vehicle Technology and Safety
Canadian institutionsMcMaster University
Fundersnot available
KeywordsAutomotive industryDistractionField (mathematics)EngineeringAutonomous system (mathematics)Computer scienceTransport engineeringArtificial intelligence

Abstract

fetched live from OpenAlex

The automotive industry is seen to be making a monumental paradigm shift from manual to semi-autonomous to fully autonomous vehicles. As such, this paper attempts to offer an overview of some of the major aspects associated with an autonomous vehicle. The demand for autonomous vehicles has grown tremendously with the increasing number of road accidents caused due to driver distraction. Based on the level of autonomy within vehicles, it is possible to either have autonomous systems that will just assist the driver for safe operation of the vehicle or could take over the complete control of the vehicle. Autonomous vehicles seem to have numerous advantages for road safety, traffic optimisation, military or medical applications where only minimal human intervention is desirable and so on. This paper attempts to highlight the vast majority of vital and most relevant topics emerging in the field of autonomous vehicle technology.

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: Other design · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.984
Threshold uncertainty score0.926

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0010.000
Research integrity0.0000.001
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 head0.273
Teacher spread0.259 · 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