Implications of Articulating Machinery on Operator Line of Sight and Efficacy of Camera Based Proximity Detection Systems
Bibliographic record
Abstract
The underground mining industry, and some above ground operations, rely on the use of heavy equipment that articulates to navigate corners in the tight confines of the tunnels. Poor line of sight (LOS) has been identified as a problem for safe operation of this machinery. Proximity detection systems, such as a video system designed to provide a 360 degree view around the machine have been implemented to improve the available LOS for the operator. A four-camera system was modeled in a computer environment to assess LOS on a 3D cad model of a typical, articulated machine. When positioned without any articulation, the system is excellent at removing blind spots for a machine driving straight forward or backward in a straight tunnel. Further analysis reveals that when the machine articulates in a simulated corner section, some camera locations are no longer useful for improving LOS into the corner. In some cases, the operator has a superior view into the corner, when compared to the best available view from the camera. The work points to the need to integrate proximity detection systems at the design, build, and manufacture stage, and to consider proper policy and procedures that would address the gains and limits of the systems prior to implementation.
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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 | 0.010 | 0.114 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 0.001 |
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".