Model-Based Estimation of Mental Workload in Drivers Using Pupil Size Measurements
Bibliographic record
Abstract
Passenger vehicles are increasingly adopting the use of automated driving systems (ADS) to help ease the workload of drivers and to improve road safety. These systems require human drivers to constantly maintain supervisory control of the ADS. For safe adoption and ADS, the attention or alertness of the driver needs to be continuously monitored. Past studies have demonstrated pupil dilation as an effective measure of cognitive load. However, the raw pupil data recorded using eye trackers are noisy which may result in poor classification of the cognitive load levels of the driver. In this paper, an approach to reduce the noise raw pupil size data obtained from eye trackers used by ADS is proposed. The proposed approach uses a Kalman filter to filter out high-frequency noise that arises due to sudden changes in ambient light, head/body movement, and measurement noise. Data collected from 16 participants were used to demonstrate the performance of the model-based pupil-size filtering approach presented in this paper. Results show an objective improvement in the potential to distinguish changes in pupil size due to various levels of cognitive workload experienced by participants.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| 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 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.004 | 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 teacher head, 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".