MétaCan
Menu
Back to cohort
Record W4400969857 · doi:10.1145/3641234.3671084

Driving Through the Data: Extended Reality Perceptual Support System (XRPSS)

2024· article· en· W4400969857 on OpenAlexaff
Jay H. Matsushiba, Mia Fitzpatrick, Nick Hedley

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldEngineering
TopicRobotics and Sensor-Based Localization
Canadian institutionsSimon Fraser University
FundersUniversitas Brawijaya
KeywordsComputer scienceVirtual realityLidarPoint cloudPerceptionProof of conceptHuman–computer interactionField (mathematics)Motion (physics)Mixed realityPoint (geometry)Computer graphics (images)Computer visionReal-time computingRemote sensingGeography

Abstract

fetched live from OpenAlex

This work introduces the Extended Reality Perceptual Support System (XRPSS) that provides real-time 3D LiDAR sensor data to off-road vehicle drivers with interactive XR for enhancing in-situ spatial awareness in natural environments. The system extends to ex-situ data exploration via motion driving simulators and VR. We created a proof-of-concept system that can both visualize real-time LiDAR point clouds in the field and generate virtual experiences for the motion simulator post-drive.

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 score0.993
Threshold uncertainty score0.325

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 head0.043
GPT teacher head0.277
Teacher spread0.234 · 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

Citations0
Published2024
Admission routes1
Has abstractyes

Explore more

Same topicRobotics and Sensor-Based LocalizationFrench-language works237,207