Role of Visual Cues from the Environment in Driving an Agricultural Vehicle
Bibliographic record
Abstract
Driving is an interactive process in which the driver receives information regarding the state of the vehicle and the environment in which the vehicle is moving through visual, motion, haptic and auditory cues.The driver needs this information for successful guidance or navigation of the vehicle.A good understanding of this process requires knowledge of the sensory cues used by the driver in performing different driving tasks.This knowledge is also necessary in the development of driving simulators which are emerging as useful research tools.The goal of this research was to test whether drivers of agricultural vehicles use visual cues from the environment when performing common driving tasks such as parallel swathing and simple turning maneuvers.Experiments were performed using a tractor in the field and using a tractor driving simulator in the laboratory.The results show that in straight line driving with a lightbar guidance system, the steering behavior and performance of most drivers does not change with varying level of visual information from the environment.However, it seemed that approximately 33% of the subjects in our experiment used an aiming cue on the field boundary, when available.Visual cues from the environment played a significant role in maneuvers which included more than one phase of steering input.Drivers were able to successfully complete those maneuvers that consisted of only one phase of steering input, such as turns, even when complete visual cues from the environment were not provided.However, maneuvers which required multiple phases of steering input could not be completed when the visual information from the environment was incomplete.A driving simulator for agricultural vehicles, therefore, should include these cues.Also, cabs of agricultural vehicles should be designed in such a way that these features can be easily seen by the operator.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 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".