Guiding Smombies: Augmenting Peripheral Vision with Low-Cost Glasses to Shift the Attention of Smartphone Users
Bibliographic record
Abstract
Over the past few years, playing Augmented Reality (AR) games on smartphones has steadily been gaining in popularity (e.g., Pokémon Go). However, playing these games while navigating traffic is highly dangerous and has led to many accidents in the past. In our work, we aim to augment peripheral vision of pedestrians with low-cost glasses to support them in critical traffic encounters. Therefore, we developed a 10-fi prototype with peripheral displays. We technically improved the prototype with the experience of five usability experts. Afterwards, we conducted an experiment on a treadmill to evaluate the effectiveness of collision warnings in our prototype. During the experiment, we compared three different light stimuli (instant, pulsing and moving) with regard to response time, error rate, and subjective feedback. Overall, we could show that all light stimuli were suitable for shifting the users' attention (100% correct). However, moving light resulted in significantly faster response times and was subjectively perceived best.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot 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.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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.
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 teacher head, 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".