Interaction Model of the Cabin of Combined Sugarcane Harvesters
Bibliographic record
Abstract
Owing to visual blind spot areas and occasional negligence, combined sugarcane harvester drivers often make mistakes in field operation, some of which evolve into major accidents. To improve drivers’ perception of and response to warning information, this paper explores the optimal interaction mode of warning information for the cabin of combined sugarcane harvesters. A series of experiments were carried out on a stationary driving simulator to verify the driver experience and alarm efficiency of three modes of warning information, namely, text, audio, and image, as well as their dual-channel modes. The physiological data, such as electrodermal activity (EDA), photoplethysmography (PPG), and electroencephalogram (EEG), of eight subjects were collected through the experiments. On this basis, the cognitive load of drivers was analyzed under different modes of warning information. The motion feedback time was recorded to parse the driver’s recognition rate and reaction speed to the warning information, and the eye movement was captured to analyze the driver’s attention distribution. The results show that the recognition rate under the dual-channel mode of visual and audio is higher than that of the single-channel mode of text or image. The addition of the visual warning information (text or image) to the audio information reduces the attention distribution time, and the best reduction effect is achieved in the image plus voice mode. The EDA indices of latency, amp sum, and mean half decay time fully reflect the effect of alarm information modes on the subjects’ reaction speed and emotional stimulation. The image plus voice mode has the fastest response speed, smallest response to stimuli, and the best ability for emotional recovery than the other modes. The eye movement, some EDA indices, and EEG are more sensitive to stress reaction, while the HRV is not sensitive for analyzing drivers’ stress to the stimuli of warning information in a short time. The research results lay the basis for designing a more efficient and accurate reminder mode of warning information for combined sugarcane harvesters.
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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) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.007 | 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".