Effects of task automation on the mental workload and situation awareness of operators of agricultural semi-autonomous vehicles
Bibliographic record
Abstract
The effects of in-vehicle automation and driving assistant systems on the mental workload and situation awareness of drivers have been the interest of many studies; some of the implications of automation in such man-machine systems have been identified. Due to the introduction of advanced automated systems in agricultural machinery, farmers are currently working with semi-autonomous vehicles. A human factors perspective on the design of these systems will ensure safe and efficient operation of such man-machine systems. In this study, a systematic approach was utilized to address human factors issues associated with operating a semi-autonomous agricultural vehicle, and to provide design recommendations. The study was carried out in three stages. First, a task analysis was used to identify tasks associated with operating an agricultural vehicle and to select appropriate experimental variables. Next, a preliminary experiment was performed to validate the test procedure and measurement techniques. Finally, the main experiment was administered. Experiments were conducted using the Tractor Driving Simulator located in the Agricultural Ergonomics Laboratory at the University of Manitoba. Thirty young experienced tractor drivers participated in this study. The experiment investigated the effects of i) vehicle steering task automation (VSTA) and ii) implement control and monitoring task automation (ICMTA) on mental workload and situation awareness of drivers. It was found that ICMTA significantly affected situation awareness (and its underlying components) of the operator. The situation awareness of drivers increased as the automation support level increased, but the highest level of automation, where the participants were out of the task loop, resulted in low situation awareness, similar to the condition with no automation support. VSTA only reduced the attentional demand of the situation, one of the three components of the situation awareness, which had negative effect on overall situation awareness. Based on the results from a subjective mental workload measure, moderate levels of mental workload were reported when the participants were involved in the implement control and monitoring task loop. The highest level of ICMTA reduced the average mental workload by 18%. Reaction time of drivers and number of errors committed by drivers both decreased as the automation level increased.
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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 | 0.008 |
| 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".