Cognitive Challenges of Human-Technology Interaction in Remote Maritime Autonomous Operations
Bibliographic record
Abstract
The integration of automation in maritime operations has revolutionised industry norms, introducing both new opportunities and challenges. Drawing on evidence from autonomous systems across other safety-critical industries, the combination of overreliance, lack of transparency in new technologies and limited knowledge of underlying decision-making processes can create an array of negative impacts for operators and thus affect overall safe and efficient operations. This chapter explores human factors in autonomous maritime operations and the cognitive demands placed on remote operators’ situation awareness. Moreover, it investigates key human-system integration challenges, including trust in automation, cognitive workload and the impact of cognitive biases on decision-making under uncertainty. Key theoretical frameworks, including situation awareness (SA), the cognitive resource-demand model and signal detection theory, are applied to analyse human-system integration. By examining the interplay between automation and human cognition, this chapter provides insights into optimising remote vessel operations. Using real-world examples and the Canadian autonomous surface vehicle case study as a focal point, this chapter advocates balancing automation with human oversight through SA-focused training, human-centred system design and AI transparency to ensure that technology complements human expertise.
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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 | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".