Discount Driver Mental Workload Assessment
Bibliographic record
Abstract
Increasingly, drivers have access to many in-vehicle applications and notifications can create interruptions during critical moments. In order to provide safe notifications to drivers, we need a way of knowing when the complexity of the driving task, or associated mental workload, is not too high. In this paper we first report on a pilot study that demonstrated the impact of mental workload on willingness to receive notifications in different driving tasks. We then report on a study where a novel method for assessing how different driving situations affect driver mental workload is developed and evaluated. We developed the “discount mental workload assessment” method, by iteratively designing simulation videos of different driving situations, along with an online questionnaire that assessed driver workload. We also identified features of driving situations that contribute to higher mental workload, as a first step towards predicting when notifications to the driver can be safely made. We found that participants were in general agreement about the level of driving mental workload associated with the different driving scenarios. We propose that the discount mental workload assessment method developed here may also be useful in other HCI (non-driving) contexts.
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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 | 0.002 | 0.011 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".