Assessing Multifunction Interfaces in Vehicles
Bibliographic record
Abstract
The goal of this research was to determine whether system usability is requisite for system safety. To this end, the usability and safety of two multifunction in-vehicle interfaces were assessed to verify the hypothesis that the system scoring highest on usability testing would also score highest on safety testing. Two multifunctional systems were subjected to ( a) heuristic evaluations to assess usability and ( b) occlusion tests to assess safety. There were more heuristic violations, indicative of more usability problems, in System B relative to System A. Similarly, with regard to safety, results of occlusion testing showed that greater demands on time and visual resources were required to perform tasks when System B was used versus System A. Thus, the usability problems identified through heuristic evaluations represent possible precursors to the safety problems identified by occlusion tests. Given the latter and the fact that heuristic evaluations can be applied quickly and easily whereas occlusion testing can be costly and time-consuming, the results of the current research suggest that developers would benefit from correcting the usability limitations of a system before its safety performance is evaluated.
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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 | 0.005 | 0.031 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".