User Interface for Unmanned Surface Vehicles Used to Rescue Drowning Victims
Bibliographic record
Abstract
This work details the look and feel of a Graphical User Interface for Unmanned Surface Vehicles (USV) used for marine mass casualty events based on feedback from the Italian Coast Guard, Los Angeles Country Fire Department (LACoFD), Pima County FD, Castrium Rescue Brigade, Department of Homeland Security, and Defense Research and Development Canada First Responders Groups. The current state of USV interface design centers on 1) ergonomic handheld devices, such as a Futaba, for teleoperation missions, or 2) non-ergonomic laptops with Mission Planner (MP) style of interfaces or teleoperation and autonomous missions. Pure, teleoperated USVs do not offer the operator needed information in terms of robot health or status. USVs with autonomous capabilities controlled and monitored via MP are not first responder friendly. Important artifacts as to the status of the vehicle are not always easily accessible. This work is a simplified, easy to use interface into the USV. This new interface combines the key artifacts in a user-friendly, tablet-based application, as well as facilitates the operator with the control and operation of the USV. This approach will first strengthen responder's trust in USVs. Second, this interface will facilitate responders during a rescue mission by simplifying the USVs control which will allow the responder to focus his or her attention to more pertinent tasks. A strengthened since of trust by first responders in using USVs will facilitate in the victim to lifeguard ratio that currently plagues responders today. The initial prototype and second version of the interface were critiqued by the Castrium Rescue Brigade and LACoFD Baywatch. The final version incorporated all feedback attained and was tested at the DHS CAUSE V Exercise in Bellingham, WA.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.044 | 0.007 |
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".