The Occlusion Procedure for Assessing in-Vehicle Telematics: Tests of Current Vehicle Systems
Bibliographic record
Abstract
Appropriate protocols are necessary for assessing the distraction potential of in-vehicle tasks and devices. The AAM (Alliance of Automobile Manufacturers) has proposed the “Statement of Principles, Criteria and Verification Procedures on Driver Interactions with Advanced In-Vehicle Information and Communication Systems” (AAM, 2003). The purpose of the present study was to assess the utility of one specific method, the Occlusion Method (as outlined by Principle 2.1 of the AAM Guidelines) by applying it to four navigation systems available in current production vehicles. This method was used to assess the distraction potential for 16 participants as they completed navigation-related tasks involving visual display and manual controls. The results provide evidence that the Total Shutter Open Time (TSOT) measure from the Occlusion Task is a promising method for discriminating between different task types and complexity levels of these tasks as well as between different telematics systems.
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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 | 0.003 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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 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".