Control and automation systems onboard the vessel: Lessons in human-centered design learned from 20 years of marine occurrences in Canada
Bibliographic record
Abstract
Vessel control and automation systems that are not designed according to human-centered design (HCD) and coding principles risk being used inappropriately or incorrectly by vessel crews or pilots. As integrated bridge and automated control systems become more common, it becomes even more important to design human-machine interfaces (HMI) that allow for effective operation and control, while providing concise feedback to aid the operator in the decision-making process. This research examined the influence of HMI design issues on the safe and effective control of vessels 150 gross tonnage or greater through a review of Transportation Safety Board of Canada (TSB) investigated marine reportable occurrences that resulted in a published TSB report. Between 1998 and 2018, 31 (16%) of 192 such TSB investigations identified one or more HMI issue as a contributing or risk factor in a marine accident. Some of the HMI issues included: non-intuitive and complex navigation system design, non-standardized controls (steering, power, propulsion, abort mechanism) and poor system feedback. Despite the availability of marine guidance on Human Factors (HF) principles from well-reputed organizations like the major classification societies and the ISO (International Organization for Standardization), the current findings demonstrate that HCD methods are not always well-understood or consistently applied to the design and modernizations of vessels. A larger effort, such as a HF in design program, is required to promote the application and understanding of HCD principles in marine system design and operations to help improve safety while reducing the potential for use error.
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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 | 0.011 | 0.018 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.005 |
| Science and technology studies | 0.009 | 0.009 |
| Scholarly communication | 0.007 | 0.003 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.001 | 0.003 |
| 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 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".