Stabilization Time after Mode Switch in Conditionally Automated Driving: Focusing on Drivers’ Cognitive Load and Visual Attention
Bibliographic record
Abstract
The switch between automated and manual driving modes is currently an inevitable topic for automated vehicles. Understanding how long it takes drivers to stabilize physically and cognitively after the driving mode switch is important to maintain driving safety. Given that little attention has been paid to drivers’ stabilization time after the driving mode switch, this study focuses on drivers’ cognitive load and visual attention and aims to investigate drivers’ stabilization time after the driving mode switch. Twenty-eight participants drove in a high-fidelity driving simulator where they experienced mode switching from manual to automated and from automated to manual. Reaction time to the detection response task and on-road fixation durations were measured throughout the experiment to assess drivers’ cognitive load and visual attention. Results revealed that it took drivers 10 to 15 s to stabilize their cognitive load after taking over the manual control of the simulated vehicle, and 5 to 10 s to stabilize after relinquishing manual driving to the automated system. These findings indicate that drivers’ cognitive load and visual attention will fluctuate after driving mode switches and a buffer time should be provided to ensure driving safety. By exploring drivers’ cognitive load and visual attention after driving mode switches, this study offers valuable insights into the design of automated driving systems and helps to improve road safety. In developing automated driving systems, efforts should be made to identify an appropriate time window for drivers to perform stable driving performance and improve their in-vehicle experience.
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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 | 0.000 | 0.004 |
| 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 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".