Analysis of Visual Scanning Patterns Comparing Drivers of Simulated L2 and L0 Systems
Bibliographic record
Abstract
Automated driving systems (ADS) partially or fully perform driving functions. Yet, the effects of ADS on drivers’ visual sampling patterns to the forward roadway remain underexplored. This study examined the eye movements of 24 young drivers during either manual (L0) or partially automated driving (L2) in a driving simulator. After completing a hazard anticipation training program, Road Awareness and Perception Training, drivers in both groups navigated a single simulated drive consisting of four environment types: highway, town, rural, and residential. Drivers of the simulated L2 system were instructed to keep their hands on the steering wheel and told that the system controls the speed and lateral positioning of the vehicle while avoiding potential threats on the forward roadway. The data indicate that the drivers produced fewer fixations during automated driving compared with manual driving. However, the breadth of horizontal and vertical eye movements and the mean fixation durations did not strongly support the null results between the two conditions. Existing hazard anticipation training programs may effectively protect drivers of partially automated systems from inattention to the forward roadway.
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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 | 0.001 |
| 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".