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Record W4386193732 · doi:10.35199/epde.2023.84

USING MULTI-LEVEL PROTOTYPING TO SHOWCASE STUDENT MOBILITY DESIGN CONCEPTS IN URBAN CONTEXTS

2023· article· en· W4386193732 on OpenAlexaff
Alejandro Lozano Robledo, Juan Antonio Islas Muñoz

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldComputer Science
TopicVirtual Reality Applications and Impacts
Canadian institutionsUniversité de Montréal
Fundersnot available
KeywordsAugmented realityUrban designScale (ratio)Computer scienceHuman–computer interactionEngineering design processFidelityProcess (computing)MultimediaPresentation (obstetrics)Architectural engineeringUrban planningEngineeringTelecommunicationsCivil engineering

Abstract

fetched live from OpenAlex

For the past century, cities evolved around car-centricity, where cars existed in uni-disciplinary isolation from urban planning in unchanging street layouts. Recently, urban planners and new paradigms are transitioning cities away from car-centrism to enable inter-modal streets for vehicles that exist today (e.g. bicycles, e-scooters, cars). Simultaneously, new types of vehicles are being designed for future cities, particularly in micromobility, which must be considered as new street layouts are designed. Therefore, new tools to communicate design concepts are required to ensure a multidisciplinary approach between mobility designers and urban planners. In mobility design, to develop vehicle concepts, students traditionally use digital (CAD) and physical prototyping, with virtual and augmented reality (AR and VR) levels recently emerging. These design concept prototypes have different degrees of fidelity, lower early in the process (e.g. small-scale appearance models or full-scale functional mock-ups), while progressively becoming more faithful to the final design (e.g. full-scale appearance models), particularly when creating the final showcase of the design concept. In academia, final mobility design showcases traditionally consist of vehicle-centric presentations where student designers prepare a verbal explanation, while audiences (usually other mobility designers) play a spectator role. The presentation consists of large posters and/or on-screen slide shows (including images, text, and animations), which can include a physical prototype (small-scale high-fidelity appearance model or low-fidelity full-scale mockup). Moreover, audiences are limited to 2D graphic and 3D physical off-scale representations of the vehicle concept with little context. After the presentation, they provide feedback, mostly addressing the vehicle’s design. VR prototypes are emerging for final showcases in mobility design education and allow audiences to transition from spectators to active participants, capable of experiencing aspects of the concept like materiality, user-interactions, and urban context around the vehicle. However, the lack of physicality of this prototyping level can be disorienting because of issues with scale, position, and visibility of the real environment and people. Thus, the low-fidelity physical level is often preferred over the virtual, even when higher-fidelity aspects of the design and the urban context are lost. AR serves as a bridge where the physical mockup audience members are sitting on, matches the environment they can see and touch through the AR cameras, and has VR geometry and interactivity overlayed on top, essentially creating a multi-level prototype experience. This approach also allows mobility designers to showcase their vehicle solutions and planners to contextualize the built environment in a seamless transition between both disciplines. Even though existing prototyping methodologies intend to bridge the physicality and virtuality of design concepts, none use the multi-level prototyping approach of AR (low-fidelity) in addition to VR (mid-fidelity) and 1:1 physical (low-fidelity) to showcase final design concepts to multidisciplinary stakeholders. This paper uses the case study of the final showcase of a Future Mobility Design Undergraduate studio focusing on micromobility. Two student micromobility concepts are demonstrated, and the findings are concluded based on the testimony of stakeholders in the AR/VR and urban development industries, who attended the event and tested the experience.

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)0.003
metaresearch head score (Gemma)0.006
Version: metacan-v3-hybrid-931329e0061cValidation 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: Empirical · Consensus signal: none
Teacher disagreement score0.023
Threshold uncertainty score0.078

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0030.006
Meta-epidemiology (narrow)0.0020.001
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0010.001
Science and technology studies0.0010.003
Scholarly communication0.0050.003
Open science0.0020.007
Research integrity0.0020.002
Insufficient payload (model declined to judge)0.0230.005

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 head0.307
GPT teacher head0.446
Teacher spread0.139 · 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 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
Published2023
Admission routes1
Has abstractyes

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