Ensuring Safety in Augmented Reality from Trade-off Between Immersion and Situation Awareness
Bibliographic record
Abstract
Although the mobility and emerging technology of augmented reality (AR) have brought significant entertainment and convenience in everyday life, the use of AR is becoming a social problem as the accidents caused by a shortage of situation awareness due to an immersion of AR are increasing. In this paper, we address the trade-off between immersion and situation awareness as the fundamental factor of the AR-related accidents. As a solution against the trade-off, we propose a third-party component that prevents pedestrian-vehicle accidents in a traffic environment based on vehicle position estimation (VPE) and vehicle position visualization (VPV). From a RGB image sequence, VPE efficiently estimates the relative 3D position between a user and a car using generated convolutional neural network (CNN) model with a region-of-interest based scheme. VPV shows the estimated car position as a dot using an out-of-view object visualization method to alert the user from possible collisions. The VPE experiment with 16 combinations of parameters showed that the InceptionV3 model, fine-tuned on activated images yields the best performance with a root mean squared error of 0.34 m in 2.1 ms. The user study of VPV showed the inversely proportional relationship between the immersion controlled by the difficulty of the AR game and the frequency of situation awareness in both quantitatively and qualitatively. Additional VPV experiment assessing two out-of-view object visualization methods (EyeSee360 and Radar) showed no significant effect on the participants' activity, while EyeSee360 yielded faster responses and Radar engendered participants' preference on average. Our field study demonstrated an integration of VPE and VPV which has potentials for safety-ensured immersion when the proposed component is used for AR in daily uses. We expect that when the proposed component is developed enough to be used in real world, it will contribute to the safety-ensured AR, as well as to the population of AR.
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 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 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 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 | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".