Combining Driving Automation Intent and Hazard Information with an Attention Reminder System: Is More Information Always Better?
Bibliographic record
Abstract
Automation can result in driver inattention and distraction, which has contributed to fatal collisions. One common safeguard implemented by automakers is “attention reminders” (ARs), which issue alerts when drivers are inattentive. Despite some observed benefits, it is unclear whether ARs alone can ensure safe operation of automation. Thus, ARs might be enhanced by combining them with displays conveying additional information, such as automation intent and surrounding hazards. A driving simulator study was conducted to evaluate the effects of combining ARs with additional information on driver visual attention (measured through gaze behavior). Forty-eight participants were assigned to one of three conditions: Baseline AR, AR + automation intent (that the automation would slow down the car), and AR + automation intent + hazard information (location & severity of potential hazard). The findings suggest that combining ARs with both automation intent and hazard information may have diverted attention away from cues in the environment indicating potential traffic conflicts, while combining the AR with automation intent only supported visual attention to the cues. However, the additional information did not show a performance benefit compared to the Baseline AR. Thus, further research should be done to investigate how to enhance ARs with additional supporting information.
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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 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".