Using the Lane-Change Test (LCT) to Assess Distraction: Tests of Visual-Manual and Speech-Based Operation of Navigation System Interfaces
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Bibliographic record
Abstract
The Lane Change Test (LCT) is an easy-to-implement, low-cost methodology for the evaluation of the distraction associated with performing invehicle tasks while driving (Mattes, 2003). In the present study, the LCT was used to assess driving performance when drivers completed navigation tasks using visual-manual or speech-based interfaces. Drivers performed two types of navigation tasks at two levels of difficulty. The results provide support for the LCT as an effective measure of distraction for both types of interface. It is recommended that the LCT procedure incorporate additional measures beyond the current mean deviation measure. Two measures are suggested: Lane Change Initiation, which reflects the aspects of driving having to do with detection and response delay as a result of distraction, and a measure of task duration to account for risk exposure.
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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 | 0.000 |
| 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) | 0.001 | 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