Perception of bimodal warning cues during remote supervision of autonomous agricultural machines
Bibliographic record
Abstract
Agricultural machines that are fully autonomous will still need human supervisors to monitor and troubleshoot system failures. Recognising the emergency as soon as possible is crucial to reduce adverse effects. The ability of humans to detect visual, auditory, or tactile cues is usually enabled by warning systems. The effectiveness of different warning cues varies in terms of prompting a quick response. The study’s objective was to compare the effectiveness of two bimodal warnings (i.e., visual-auditory and visual-tactile) at eliciting supervisor perception (which equates to level one situation awareness). Twenty-five participants engaged in an autonomous sprayer simulation. Two realistic remote supervision scenarios (i.e., in-field and close-to-field) were used to examine two bimodal warning cues: (i) visual-auditory and (ii) visual-tactile. The effectiveness of each bimodal warning was assessed based on two measures: (i) response time and (ii) noticeability. There was no significant difference between the bimodal warning cues in terms of response time when tractor sound was present in the experimental environment (reflecting the in-field remote supervision scenario); however, visual-tactile cues yielded shorter response times than visual-auditory cues when the experimental environment was quiet (reflecting the close-to-field remote supervision scenario). There were no statistically significant differences between visual-auditory and visual-tactile warnings concerning noticeability. Participants’ subjective answers indicated they preferred the visual-tactile cues better than the visual-auditory cues. It is concluded that visual-tactile warnings are preferred over visual-auditory warnings to enable perception during remote supervision of autonomous agricultural machines (AAMs).
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.007 |
| 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 | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".