Exploring the Impact of Immersion on Situational Awareness and Trust in Remotely Monitored Maritime Autonomous Surface Ships
Bibliographic record
Abstract
Consistent with the International Maritime Organisation’s roadmap for regulating the operation of autonomous surface ships, most concepts of operations for crewed and uncrewed autonomous shipping rely on monitoring and operation from a Remote Control Centre (RCC). The successful execution of such activities requires that operators have adequate Situational Awareness (SA), while avoiding situations of information overload, and the right amount of, or calibrated, Trust in the system. In this study, we examined how operator SA and Trust were affected by different levels of Immersion of the Human-Machine Interface (HMI). Simulated RCC interfaces were constructed for a scenario where an autonomous container ship traversed the arctic escorted by robotic aids. SA, Trust, and Motion Sickness (MS) were tracked over time. Different Virtual Reality (VR) technologies were used to represent three levels of Immersion: Non-Immersive VR (NVR), Semi-Immersive VR (SVR), and Immersive VR (IVR). The results illustrated various trade-offs – with NVR shown to be less taxing, SVR showing several potential benefits for SA, and IVR showing a strong relationship between Trust and SA accuracy, but increased MS. These results suggest that Immersion is an important factor in Situational Awareness and Trust in automation; future research should consider both the extent of Immersion, potential for MS, and the format of delivery (e.g. head-mounted displays versus immersive projection displays). Understanding these trade-offs between levels of Immersion is a requisite step for designing RCCs.
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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 | 0.010 |
| 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 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 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".