Effects of Hybrid Interface on Ecodriving and Driver Distraction
Bibliographic record
Abstract
Hybrid interfaces are emerging in-vehicle technologies that have received minimal research attention in the literature concerning potential impacts on driver performance. Hybrid interfaces have the potential to improve driver fuel efficiency but also to distract drivers. Participants drove a number of urban and suburban routes in the University of Calgary Driving Simulator while interacting with a hybrid interface and attempting to drive fuel efficiently. A within-subjects design was used and each participant drove with and without the hybrid interface. With the hybrid interface, significant reductions in acceleration from a stop were observed when compared with driving without the hybrid interface. Participants spent significantly less time looking to the road ahead while driving with the interface than without it. The duration of participant eye glances to the interface did not exceed 1.6 s; however, drivers tended to combine glances to the interface and speedometer. This combination resulted in several glance durations above 1.6 s without looking back to the road ahead. Balancing ecodriving benefits with driver distraction costs is discussed.
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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) | 0.000 | 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) | 0.004 | 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".