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Record W3175009974 · doi:10.1145/3461778.3462068

Co-Designing Interactions between Pedestrians in Wheelchairs and Autonomous Vehicles

2021· article· en· W3175009974 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

Venuenot available
Typearticle
Languageen
FieldComputer Science
TopicGaze Tracking and Assistive Technology
Canadian institutionsUniversity of VictoriaUniversity of Calgary
Fundersnot available
KeywordsTestbedWheelchairHuman–computer interactionComputer scienceWork (physics)Interface (matter)Universal designTransport engineeringSimulationEngineeringWorld Wide Web

Abstract

fetched live from OpenAlex

In the near future, mixed traffic consisting of manual and autonomous vehicles (AVs) will be common. Questions surrounding how vulnerable road users such as pedestrians in wheelchairs (PWs) will make crossing decisions in these new situations are underexplored. We conducted a remote co-design study with one of the researchers of this work who has the lived experience as a powered wheelchair user and applied inclusive design practices. This allowed us to identify and reflect on interface design ideas that can help PWs make safe crossing decisions at intersections. Through an iterative five-week study, we implemented interfaces that can be placed on the vehicle, on the wheelchair, and on the street infrastructure and evaluated them during the co-design sessions using a VR simulator testbed. Informed by our findings, we discuss design insights for implementing inclusive interfaces to improve interactions between autonomous vehicles and vulnerable road users.

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: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.443
Threshold uncertainty score0.309

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 head0.030
GPT teacher head0.287
Teacher spread0.257 · 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

Quick stats

Citations30
Published2021
Admission routes1
Has abstractyes

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