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Record W2889913703 · doi:10.2514/1.g003526

Online Risk-Based Supervisory Maneuvering Guidance for Small Unmanned Aircraft Systems

2018· article· en· W2889913703 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

VenueJournal of Guidance Control and Dynamics · 2018
Typearticle
Languageen
FieldEngineering
TopicAir Traffic Management and Optimization
Canadian institutionsMemorial University of Newfoundland
Fundersnot available
KeywordsAerospace engineeringAeronauticsComputer scienceSupervisory controlGuidance systemControl engineeringEngineeringArtificial intelligenceControl (management)

Abstract

fetched live from OpenAlex

To achieve a level of safety equivalent to manned aircraft in the national airspace system, the pilot-in-command of small unmanned aircraft systems should be able to maintain situational awareness and make necessary maneuvers to avoid potential conflicts with nearby air traffic during mid-air encounters. However, due to ever-changing flight environments, the pilot-in-command often mis-estimates collision risks and cannot engage appropriate maneuvers to prevent mid-air traffic from violating the safety boundaries of small unmanned aircraft systems. To fix this problem, an online risk-based guidance method is therefore designed and developed in this paper to quantitatively assess mid-air collision risks and provide online mitigation solutions for detect-and-avoid systems. This will help the pilot-in-command identify hazards and choose appropriate avoidance maneuvers before a safety boundary violation occurs.

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: Simulation or modeling
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.846
Threshold uncertainty score0.619

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 head0.011
GPT teacher head0.203
Teacher spread0.192 · 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