Kalman Filtering to Track Changes in Pupil Size for Automated Driving Systems
Bibliographic record
Abstract
Automation has become indispensable in all walks of everyday life. In driving environments, Automated Driving Systems (ADS) aid the driver by reducing the required workload and by improving road safety. These systems require human drivers to remain vigilant and maintain supervisory control over ADS. Therefore, the cognitive attention of the drivers must be estimated accurately for the safe adoption of ADS. Because of the non-invasive recording setup used in low-cost infrared eye-trackers, pupil size measurements are increasingly becoming applicable in the estimation of cognitive load. However, pupil size measurements are highly noisy, resulting in the poor classification of cognitive load levels. In this paper, we propose a methodology for improved classification of changes in cognitive load through the introduction of a state-space model-based approach to filter the pupil size data. The proposed approach was demonstrated on data collected from 16 participants while they performed driving task and several secondary tasks that are designed to emulate three different levels of driving distraction.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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; both teacher heads agree on what is shown here.
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".