Driver Inattention Detection in the Context of Next-Generation Autonomous Vehicles Design: A Survey
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Driver inattention is among major contributing factors to traffic accidents. There have been and continue to be efforts by governing bodies, car manufacturers, and researchers to prevent driver inattention or, failing that, to mitigate its effects. Many vehicles nowadays come equipped with driver monitoring systems that can alert the driver to, or compensate for, inattention. Moreover, the research community continues to explore and investigate more robust approaches to deal with inattention. Meanwhile, vehicle automation, to various degrees, is becoming more prevalent, with the human's role in the driving task changing depending on the level of autonomy. This necessitates that inattention detection, moving forward, be studied and designed in view of automation and in the context of a specific level of vehicle autonomy. Driver inattention and vehicle automation interact in a complex way, and that needs to be taken into account in the design of future vehicles. We explore this interaction in this paper in light of research findings, and survey inattention detection systems and attempt to contextualize them within popular frameworks for next-generation autonomous vehicles.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| 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) | 0.000 | 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 it