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Record W4231720604 · doi:10.1002/rob.20344

Vision‐based autonomous convoying with constant time delay

2010· article· en· W4231720604 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.
aboutThe title or abstract carries a Canadian signal from the geographic lexicon.

Bibliographic record

VenueJournal of Field Robotics · 2010
Typearticle
Languageen
FieldComputer Science
TopicRobotic Path Planning Algorithms
Canadian institutionsDefence Research and Development CanadaUniversity of Toronto
Fundersnot available
KeywordsHeading (navigation)TrajectoryConstant (computer programming)Tracking (education)Track (disk drive)Computer scienceField (mathematics)Control theory (sociology)Real-time computingSimulationEngineeringArtificial intelligenceControl (management)Aerospace engineeringMathematics

Abstract

fetched live from OpenAlex

Abstract This paper describes the design and experimental validation of a vision‐based vehicle‐following system that uses only onboard sensors to enable a convoy of follower vehicles to autonomously track the trajectory of a manually driven lead vehicle. The tracking is done using the concept of a constant time delay, in which a follower tracks the delayed trajectory of its leader. This constant‐time‐delay approach allows for new techniques to be used to estimate the speed and heading of the leader. Experiments were conducted with full‐sized military vehicles on a 1.3‐km test track. Successful field trials with one follower for 10 laps and with two followers for 13.5 laps, totaling over 30 km, are presented. © 2010 Government of Canada. Exclusive worldwide publication rights in the article have been transferred to Wiley Periodicals, Inc., AWiley Company.

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)0.001
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: Methods · Consensus signal: Methods
Teacher disagreement score0.171
Threshold uncertainty score0.427

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
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 science0.0010.000
Research integrity0.0000.001
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 head0.008
GPT teacher head0.245
Teacher spread0.236 · 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