Opacity in Car Augmented Reality Head-up Displays: Users’ Preferences, Visual Attention, and Situation Awareness
Bibliographic record
Abstract
Car augmented-reality heads-up displays (AR-HUDs) superimpose driving information on the outside view.However, there are concerns that AR-HUDs may be distracting and questions remain about how best to present AR-HUD imagery.This thesis explores people's opinions about AR-HUD opacity and its effects on driver distraction.Videos of driving scenes were created and navigation-related AR-HUD imagery was added at different levels of opacity.Twenty-seven participants watched a set of videos while their eye movements were tracked.Participants had to answer questions related to the opacity of the AR-HUD imagery and to their situation awareness.Results suggest that opacity affects how people look at and perceive the roadside environment, an opacity level of 60% is the best option for central AR-HUD imagery, and the effects of AR-HUD opacity are different for different types of AR-HUD imagery.Thus, the AR-HUD opacity level needs to be further investigated to maximize driving safety.
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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 | 0.001 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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 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".