The distraction potential of driving a partially automated vehicle through a construction zone
Bibliographic record
Abstract
Partial driving automation is designed to control the vehicle's speed and acceleration without input from the human driver on the condition that the driver maintains alertness. These systems are promised to make driving more convenient and safer, especially in increasingly demanding road conditions such as construction zones. Despite this, little knowledge is available on how these systems are used in these accident-prone areas and the effect they may have on drivers' workload and glance allocation. This study aims to fill this gap by having participants drive a vehicle in partially automated and manual mode through three road sections: pre-construction, construction, and post-construction. Results show no differences in cognitive workload by driving mode or construction zone. An increase in glances directed away from the forward roadway toward the vehicle's touchscreen was observed during partially-automated driving in the pre-construction zone, a pattern that, notably, continued on when driving throughout the construction zone. These findings adds to the literature on the human factors of partial automation. More importantly, because drivers failed to increase the amount of time looking at the forward roadway when entering the construction zone, they show the potential perniciousness of partially automated driving and the detrimental effect certain systems may have on safety risk.
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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 | 0.001 | 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 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".