A User Interface Design Framework for Augmented-Reality-Supported Maritime Navigation
Bibliographic record
Abstract
Augmented reality (AR) technology has emerged as a promising solution that can potentially reduce head-down time and increase situational awareness during navigation operations. It is also useful for remote operation centers where video feeds from remote ships can be “augmented” with data and information. In this article, we introduce a user interface design concept that supports ship navigation by showing data about points of interest in AR. This approach enables users to view and interact with relevant data in the maritime environment by bridging the gap between digital information and real-world features. The proposed concept can provide operational data from various maritime systems, such as radar, GPS, AIS, or camera systems, empowering users with a wealth of information about their surroundings. Developed through an iterative user-centered design process, it was built as an extension to the OpenBridge design system, an open-source platform facilitating consistent design in maritime workplaces. Furthermore, we use this concept to propose a design framework that paves the way for establishing new standards for AR user interface design in the maritime domain.
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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 | 0.006 | 0.006 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.004 | 0.003 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.007 | 0.003 |
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".