Introducing AV-Sketch: An Immersive Participatory Design Tool for Automated Vehicle — Passenger Interaction
Bibliographic record
Abstract
In the emerging automated vehicle (AV)—passenger interaction domain, there is no agreed-upon set of methods to design early concepts. Non-designers may find it challenging to brainstorm interfaces for unfamiliar technology like AVs. Therefore, we explore using an immersive virtual environment to enable expert and non-expert designers to actively participate in the design phases. We built AV-Sketch, an in-situ (on-site) simulator that allows the creation of automotive interfaces while being immersed in VR depicting diverse AV-passenger interactions. At first, we conducted a participatory design study (N=15) by utilizing PICTIVE (Plastic Interface for Collaborative Technology) to conceptualize human-machine interfaces for AV passengers. The findings led to the design of AV-Sketch, which we tested in a design session (N=10), assessing users’ design experiences. Overall, participants felt more engaged and confident with the in-situ experience, enabling better contextualization of design ideas in real-world scenarios, with improved spatial considerations and dynamic aspects of in-vehicle interfaces.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.008 | 0.013 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.002 | 0.005 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 0.001 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".