Assessing Driver Task Engagement Through Machine Learning Classification of Physiological Response
Bibliographic record
Abstract
As autonomous driving features become more prevalent in the automotive industry, the need to assess driver engagement becomes crucial to ensure safety and facilitate a smooth transition from human-controlled to autonomous driving. Until level 5 autonomous vehicles (AVs) have achieved full adoption in the automobile market, drivers will still be required to supervise and potentially take over control of their AVs. While this is the case, drivers need to maintain an appropriate level of engagement, even when they are not driving. The following study outlines a method to apply ambient sensors to measure drivers' physiological states. The measurements collected by the ambient sensors were converted to time series metrics. These metrics were then analyzed by machine learning (ML) methods to classify drivers as driving in either a low or high engagement driving situation, where engagement was moderated by the introduction of a surprise event. AI explainability methods were used in conjunction with the ML models to understand the key physiological factors that contributed to understanding a driver's level of engagement. This research contributes to the advancement of sensor-based monitoring systems for driver engagement, which can enhance safety in the transportation sector.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| 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".