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Record W4311800433 · doi:10.1167/jov.22.14.4058

Effects of Blur on Duration Thresholds for Road Hazard Detection

2022· article· en· W4311800433 on OpenAlexaff
Silvia Guidi, Chandandeep Ghuman, Anna Kosovicheva, Benjamin Wolfe

Bibliographic record

VenueJournal of Vision · 2022
Typearticle
Languageen
FieldEngineering
TopicTraffic and Road Safety
Canadian institutionsUniversity of Toronto
Fundersnot available
KeywordsHazardDuration (music)Hazard ratioAudiologyTruckPsychologyProportional hazards modelBlurred visionStatisticsMedicineEngineeringMathematicsAutomotive engineeringConfidence intervalPsychiatryArt

Abstract

fetched live from OpenAlex

How does the loss of visual acuity impact our ability to notice dangerous situations on the road, and might it impact particular kinds of hazards more? With age, visual performance declines, which impacts road safety since certain hazards may become harder to detect. In this study, we investigate the impact of blur on duration thresholds in a hazard detection task for older and younger drivers. Older participants (ages 55-70) and younger participants (ages 20-35) were shown blurred or non-blurred videos from the Road Hazard Stimuli and reported whether a hazard was present in the video. Video duration ranged from 67 to 1067 ms (2 to 32 frames). To determine duration thresholds, video duration on each trial was manipulated with a staircase procedure. In Experiment 1, observers simply determined whether a hazard was present in both blurred and non-blurred conditions, with separate staircases for each. As in prior results (Wolfe et al. 2020), older observers required longer viewing durations, but blur resulted in similar increases in threshold for older and younger observers (+78 ms, p<0.001). In a second experiment, we separated our hazard videos into two categories: vehicular (cars, trucks) and nonvehicular (pedestrians, cyclists, animals) categories, and independently determined hazard duration thresholds for each category across blurred and unblurred conditions and across older and younger observers. Duration thresholds were lower for nonvehicular hazards compared to vehicular hazards (p<0.001). We also found a larger effect of blur for nonvehicular hazards (+146 ms) compared to vehicular hazards (+53 ms), p=0.003. This result suggests that hazards defined by their higher spatial frequency content, like nonvehicular hazards, are more affected by blur. Our results indicate that loss of visual acuity has profound consequences for driver safety across age groups, and particularly for the safety of non-vehicular road users like pedestrians and cyclists.

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 machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.012
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.003
Threshold uncertainty score0.009

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.012
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0010.001
Open science0.0000.001
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0030.000

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 head0.005
GPT teacher head0.228
Teacher spread0.223 · 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 source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designBench or experimental
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

Citations2
Published2022
Admission routes1
Has abstractyes

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