A Probabilistic Model for Visual Driver Gaze Approximation from Head Pose Estimation
Why this work is in the frame
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Bibliographic record
Abstract
The direction of a vehicle driver's visual attention plays an essential role in the research on Advanced Driving Assistance Systems (ADAS) and autonomous vehicles. How a driver monitors the surrounding environment is at least partially descriptive of the driver's situational awareness. While driver gaze is not explicitly related to head pose due to the interplay between head and eye movements, it may still provide an approximation of the visual attention that is sufficiently accurate for many applications. In this research, we propose a probabilistic method for describing the visual attention of drivers. This method applies a Gaussian Process Regression (GPR) technique that estimates the probability of the driver gaze direction, given head pose. We evaluate our model on real data collected during drives with an experimental vehicle in urban and suburban areas. Our experimental results show that 82.5% of drivers' gaze lies within the 95% confidence interval predicted by our framework.
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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 | 0.000 | 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 | 0.001 |
| 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