Understanding the requirements for a blind-spot monitoring system on tractors from the operator’s perspective
Bibliographic record
Abstract
Unintentional run-overs occur because the operator of a tractor is unable to physically see all around the machine. Therefore, there is a need to devise an effective blind-spot monitoring system for tractors to prevent unintentional run-overs. The purpose of the study was to identify the locations of blind spots around two types of tractors (i.e., with and without a front-end loader), with the ultimate goal of conceptualizing a blind-spot monitoring system capable of eliminating all existing blind spots. Grids were constructed around all four sides of the tractors to determine the presence of blind spots for drivers of varying sitting height (i.e., 5th, 50th and 95th percentile male for erect and slumped postures) at four horizontal planes representing people of varying stature who might be in the vicinity of the tractor (i.e., standing male, standing female, standing child, kneeling adult). Generally, the proportion of markers not visible decreased as the sitting height increased. Differences between erect and slumped sitting postures were not statistically different suggesting this variable could be ignored in the assessment of blind spots around tractors. The proportion of the markers not visible to the operator varied from 0 to 34%, with higher values observed for the tractor with the front-end loader installed. Values were as high as 42% of the markers not visible for the condition where a passenger was present in the passenger/trainer seat. Use of the existing rear-view mirrors eliminated only a small fraction of the blind spot area around the tractors. Through trial and error, it was determined that five and eight cameras would be required to fully detect the entire blind spot area around the two tractors selected for this study. A blind-spot monitoring system composed of five or eight cameras would create substantial additional monitoring burden for the tractor operator and, therefore, is not a feasible solution. A hybrid blind-spot monitoring system consisting of cameras and proximity sensors warrants further investigation.
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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 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.003 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.004 | 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".