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Record W4225244079 · doi:10.1109/access.2022.3168320

Future Trends in Connected and Autonomous Vehicles: Enabling Communications and Processing Technologies

2022· article· en· W4225244079 on OpenAlex
Issam Damaj, Jibran Yousafzai, Hussein T. Mouftah

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

VenueIEEE Access · 2022
Typearticle
Languageen
FieldEngineering
TopicVehicular Ad Hoc Networks (VANETs)
Canadian institutionsUniversity of Ottawa
Fundersnot available
KeywordsComputer scienceSoftware deploymentVariety (cybernetics)ArchitectureState (computer science)Focus (optics)Vehicular communication systemsCommunications systemSet (abstract data type)TelecommunicationsEmbedded systemSystems engineeringDistributed computingVehicular ad hoc networkSoftware engineeringWirelessEngineeringWireless ad hoc network

Abstract

fetched live from OpenAlex

With significant advancements in information and communication technologies, connected and autonomous vehicles (CAVs) can provide improved transportation services. At present, a variety of technologies, such as vehicular networks, communication interfaces, and modern hardware devices enable CAVs to support reliable, safe, and quality transportation system options with improved performance and increased effectiveness. In this paper, we carefully explore a set of distinguished state-of-the-art CAV systems with a focus on On-board Computational Unit (OBCU) hardware architectures, communication technologies, deployment challenges, and performance aspects. The exploration critically identifies important area transformations and anticipates future trends influencing CAV communications and processing requirements. To that end, we propose the design of a future generic OBCU architecture that can be customized with appealing features and used in CAVs.

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 score0.659
Threshold uncertainty score0.548

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
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 head0.019
GPT teacher head0.262
Teacher spread0.244 · 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