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Record W2756315406 · doi:10.3141/2621-03

Vehicle-to-Pedestrian Communication Modeling and Collision Avoiding Method in Connected Vehicle Environment

2017· article· en· W2756315406 on OpenAlexaff
Shuxian He, Jiangchen Li, Tony Z. Qiu

Bibliographic record

VenueTransportation Research Record Journal of the Transportation Research Board · 2017
Typearticle
Languageen
FieldEngineering
TopicVehicular Ad Hoc Networks (VANETs)
Canadian institutionsUniversity of Alberta
Fundersnot available
KeywordsDedicated short-range communicationsPedestrianBluetoothComputer scienceWirelessCollisionVehicular communication systemsControl (management)Computer networkCollision avoidanceReal-time computingEmbedded systemSimulationEngineeringVehicular ad hoc networkTransport engineeringTelecommunicationsComputer securityWireless ad hoc networkArtificial intelligence

Abstract

fetched live from OpenAlex

A generalized framework consisting of a stochastic model, a responsive control method, and typical scenarios using vehicle-to-pedestrian (V2P) communication is proposed to improve pedestrian safety. The proposed stochastic model formulates various effects of uncertainties in a V2P communication system. The responsive control method is used to improve pedestrian safety under V2P communication conditions. Results show that adopting only Bluetooth or Wi-Fi (wireless local area network) technology, which has a high establishment time, is not sufficient for V2P communication, whereas dedicated short-range communication (DSRC) featuring low latencies meets requirements. A case study in the field was conducted to evaluate the feasibility of responsive control; it was concluded that with the use of the proposed model and the responsive control method, Bluetooth technology combined with DSRC could be workable for active pedestrian protection.

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)0.008
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesResearch integrity
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 score0.438
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0080.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0010.000
Scholarly communication0.0000.001
Open science0.0010.000
Research integrity0.0000.003
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 head0.079
GPT teacher head0.364
Teacher spread0.285 · 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.

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

Citations23
Published2017
Admission routes1
Has abstractyes

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