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Record W2970089253 · doi:10.1109/ivs.2019.8814107

GeoScenario: An Open DSL for Autonomous Driving Scenario Representation

2019· article· en· W2970089253 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

Venuenot available
Typearticle
Languageen
FieldComputer Science
TopicModel-Driven Software Engineering Techniques
Canadian institutionsUniversity of Waterloo
Fundersnot available
KeywordsDigital subscriber lineComputer scienceDomain-specific languageCover (algebra)Domain (mathematical analysis)Representation (politics)Scenario testingTest (biology)Simple (philosophy)Human–computer interactionSoftware engineeringSimulationVariety (cybernetics)Artificial intelligenceEngineeringComputer network

Abstract

fetched live from OpenAlex

Automated Driving Systems (ADS) require extensive evaluation to assure acceptable levels of safety before they can operate in real-world traffic. Although many tools are available to perform such tests in simulation, the lack of a language to formally capture test scenarios that cover the complexity of road traffic situations hinders the reproducibility of tests and impairs the exchangeability between tools. We propose GeoScenario as a Domain-Specific Language (DSL) for scenario representation to substantiate test cases in simulation. By adopting GeoScenario in the simulation infrastructure of a self-driving car project, we use the language in practice to test an autonomy stack in simulation. The language was built on top of the well-known Open Street Map standard, and designed to be simple and extensible.

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: Methods · Consensus signal: Methods
Teacher disagreement score0.931
Threshold uncertainty score0.554

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 communication0.0010.002
Open science0.0020.001
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 head0.028
GPT teacher head0.304
Teacher spread0.276 · 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