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Record W3210732437 · doi:10.1109/iv48863.2021.9575586

Opportunistic Strategy for Cooperative Maneuvering Using Conflict Analysis

2021· article· en· W3210732437 on OpenAlexaff
Hao M. Wang, Sergei S. Avedisov, Ahmed Hamdi Sakr, Onur Altintas, Gábor Orosz

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldEngineering
TopicTraffic control and management
Canadian institutionsUniversity of Windsor
Fundersnot available
KeywordsComputer scienceAutomationOrder (exchange)Mechanism (biology)Id, ego and super-egoControl (management)Operations researchArtificial intelligenceEngineering

Abstract

fetched live from OpenAlex

In this paper, we propose an optimization-based strategy that utilizes vehicle-to-everything (V2X) communication in order to resolve conflicts between vehicles of different automation levels. The strategy consists of a decision checking mechanism and a control law to adjust the decision of an ego vehicle in a certain maneuver based on status update messages received from a remote vehicle involved in that maneuver. Using numerical simulations with real highway data, we demonstrate the proposed opportunistic strategy and show how it improves safety and maximizes the time efficiency of the ego vehicle. We also highlight the benefits of the strategy by comparing the results with an existing conservative strategy.

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)0.000
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: none
Teacher disagreement score0.952
Threshold uncertainty score0.373

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 head0.060
GPT teacher head0.274
Teacher spread0.213 · 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

Citations4
Published2021
Admission routes1
Has abstractyes

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