An Empirically Based Suggestion for Reformulating the Glance Duration Criteria in NHTSA's Visual-Manual Interaction Guidelines
Bibliographic record
Abstract
NHTSA recently proposed performance guidelines for visual-manual interaction with non-driving related in-vehicle systems. While a commendable effort to reduce distraction related crashes, in part they seem overly strict. In particular, NHTSA proposes that for each driver performing a secondary task, no more than15 % of the off-road eye glances can be longer than 2.0 s, and 21 in 24 drivers must meet this criterion. The applicability of this criterion was assessed in a study using data from two eye-tracker based studies, involving 35 subjects performing a range of secondary tasks on normal roads. Results showed that over tasks, the average off-road glance duration lengths were quite robust within drivers but varied widely between drivers. Off-road glance duration length thus seems more to reflect individual driver attention allocation strategy than in-vehicle task complexity. Also, several drivers failed to meet the suggested criterion. Assuming that their relative prevalence can be generalized to the general driver population, then as many as one in six drivers may display the type of naturally long off-road glances that will make them fail to meet the criterion. It follows that any task tested by a group of randomly selected drivers likely will fail, since the suggested performance criterion does not allow for this natural driver variability. As currently written, the proposed compliance testing thus risks disqualifying many in-vehicle systems independently of how well they are designed. The criterion therefore needs to be reformulated, e.g., by measuring compliance on a group level rather than on an individual level.
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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 | 0.031 | 0.088 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.004 | 0.003 |
| Science and technology studies | 0.002 | 0.003 |
| Scholarly communication | 0.005 | 0.004 |
| Open science | 0.007 | 0.003 |
| Research integrity | 0.008 | 0.010 |
| Insufficient payload (model declined to judge) | 0.009 | 0.006 |
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".