Human Automation Coordination: Supporting Driver Takeover during Predictable and Unpredictable Automation Failures
Bibliographic record
Abstract
Today’s vehicles are becoming highly automated, changing the driver’s task from one of purely driving to one of monitoring the automation. Drivers may fail to monitor the automation, and therefore when drivers are forced to react to unexpected automation failures, they exhibit worse performance than manual driving. Through a driving simulator experiment, this thesis aims to 1)understand how different types of automation failure events, whether predictable or unpredictable, impact the driver’s takeover performance, and 2)compare driver takeover performance when using different displays, specifically the Takeover Request (TOR) and the reliability display. Findings show that drivers put their hands on the wheel sooner and have greater situation awareness for predictable failures; drivers appear to takeover sooner and have a better takeover quality during predictable failures when the reliability display is present than when TOR is present. As compared to no display, both displays provide a benefit to the driver’s takeover performance.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".