Ergonomic evaluation of visual guidance aids for agricultural machines
Bibliographic record
Abstract
A variety of guidance aids for agricultural machines are available on the market. The visual guidance aid is considered to be the most useful tool for further development. Currently, the main concerns with using the visual guidance aid are the placement of the camera and the driver mental workload caused by the introduction of another monitor into the tractor cab. The objectives for this thesis, therefore, were to determine the optimum placement of a video camera to minimize lateral error and to determine a relationship between lateral error and driver mental workload. To achieve these goals, an experiment was conducted in the field with a visual guidance aid. Two measurements, lateral error and driver subjective scores, were recorded and later analyzed. Based on results of both lateral error and subjective scores, it was concluded that, to achieve a lateral error less than 200 mm, a guidance camera should be placed 1.5 m above the ground and tilted downward at 30. Furthermore, a camera with a 20 lateral field of view is more appropriate than a camera with a 39 lateral field of view. To explain the relationship between driver mental workload and lateral error, two concepts, lateral ratio and image velocity were defined based on geometric relationships. Two hypotheses were proposed based on the experimental results. First, it was hypothesized that the driver mental workload would increase as image velocity increased. Second, it was hypothesized that the magnitude of lateral errors would increase as the lateral ratio increased. Because the image velocity is inversely proportional to the lateral ratio, it may be necessary to find a compromise between driver mental workload and the lateral error. In other words, to achieve a reasonable lateral error, a driver must tolerate a certain workload.
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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 | 0.005 |
| 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 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".