Using Glance Behaviour to Evaluate ACC Driver Controls in a Driving Simulator
Bibliographic record
Abstract
This paper examines the glance behaviour of drivers while interacting with two different driver-vehicle interface concepts for an Adaptive Cruise Control (ACC) system. With the integrated concept, the speed and following distance controls were located on the steering wheel whereas with the divided concept the speed control was moved to the dashboard. A virtual Head-Up Display (HUD) was used to show the ACC settings and current speed. Twelve subjects (19 to 53 years old) drove a rural road course in a fixed-base driving simulator while being verbally instructed to adjust the speed and/or following distance of the ACC system. Dividing the controls between the steering wheel and dashboard caused significantly larger mean and maximum glance times and a lower glance frequency to the displays and controls. The percent glance time “off-road” furthermore increased significantly during task completion. Other significant results were observed between the task type and task length.
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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 | 0.001 | 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) | 0.000 | 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 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".