Assessing Driver Gaze Location in a Dynamic Vehicle Environment
Bibliographic record
Abstract
Measuring the human gaze is an important area of research due to this measurement's ability to give insight into what or where a person is focused and/or paying attention to. However, gaze has been very challenging to measure effectively and then convert into a metric. The problem of measuring human gaze is challenging in the context of dynamic environments with motion of the subject or the environment itself. One domain of research that has sought these gaze-related attention metrics has been the area of automotive driver assessment. Being able to understand if a driver is looking at relevant areas as well as scanning the road for hazards is a valuable metric to evaluate if an individual is fit to drive. Eye-tracking glasses measure where a person is looking relative to their head position but do not map this information against important regions within the visual field. This paper provides a computationally scalable method to identify relevant regions within a dynamic visual field and allow for the measurement of what a driver is focused on, reducing the need for extensive manual segmentation. The paper provides a method of identifying the windshield and other key regions within a motor vehicle typical for a driver's field of view. The identification of key regions was accomplished through the application of convolutional neural networks (CNNs) with a Dice score of 0.9404 The model is then shown to allow for the assessment of visual focus for drivers.
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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 | 0.001 |
| 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 | 0.001 |
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; 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".