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Record W4386802583 · doi:10.23977/jaip.2023.060509

Intelligent Following Car Based on Dual Detection Positioning Using Ultrasonic and Camera

2023· article· en· W4386802583 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.

venuePublished in a venue whose home country is Canada.
no affNo Canadian affiliation: this work is invisible to an affiliation-only frame.
No Canadian affiliation. An affiliation-only frame, the usual design, would never have seen this work. It is one of the works that make the case for inverting the frame.

Bibliographic record

VenueJournal of Artificial Intelligence Practice · 2023
Typearticle
Languageen
FieldComputer Science
TopicAdvanced Technologies in Various Fields
Canadian institutionsnot available
Fundersnot available
KeywordsUltrasonic sensorComputer visionComputer scienceArtificial intelligenceKalman filterPositioning systemObject detectionPosition (finance)Control unitTracking (education)Object (grammar)Video trackingReal-time computingEngineeringPattern recognition (psychology)Acoustics

Abstract

fetched live from OpenAlex

To achieve real-time positioning of the target object for given tasks, a smart car system is designed using STM32F407 as the core control unit, mainly relying on ultrasonic sensors for detection and supplemented by camera detection for target locating and tracking. The ultrasonic sensor module is used as the main device for target information collection and transmission, and the distance difference between the two modules and the follow-up object is used to determine the target position. Meanwhile, the camera module is used for accurate positioning of the target, and algorithms such as Kalman filtering and PID closed-loop control are used to achieve interaction between the two detection modules and intelligent following. By doing so, the system can always maintain a distance from the target person and provide timely assistance as needed.

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)0.006
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: none
Teacher disagreement score0.768
Threshold uncertainty score0.713

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.006
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0000.002
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 head0.049
GPT teacher head0.346
Teacher spread0.297 · 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