Measuring Distraction: Task Duration and the Lane-Change Test (LCT)
Bibliographic record
Abstract
Considerable research activity (e.g., HASTE, CAMP, ADAM projects) is currently focused on producing protocols for assessing the distraction potential of in-vehicle tasks and devices. The Lane Change Test (LCT) is a relatively simple and low cost standardized test scenario designed for measuring driver distraction. The purpose of the present study was to evaluate the LCT's ability to discriminate between different secondary tasks with different levels of workload. The LCT was used to assess the driving performance of twenty-one drivers while they performed typical navigation tasks, Point of Interest (POI) Entry and Destination Entry, each with a low and high workload version. The experimental set up included a steering wheel, foot pedals, monitor, computer and navigation system, all off the shelf. The results indicated that the LCT is a sensitive measure of driver distraction. The participants showed greater mean deviation in lane change path when driving while performing a secondary task (i.e., calibration and navigation tasks) than when driving without performing a secondary task (i.e., baseline). When driving while performing secondary tasks, drivers showed differences in lane change path deviations as a function task type and task complexity. These differences were also reflected in participants mean task time to complete the secondary tasks. The present research provides evidence that the LCT metric of lane change path deviations discriminates between different types and complexity levels of secondary tasks, and that these differences are a function of time taken to complete the secondary tasks.
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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 | 0.002 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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 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".