Investigation of driver preference for a user-centred design of decision systems in autonomous vehicles, part I: preferences for binary self-driving modes
Bibliographic record
Abstract
As autonomous vehicles (AV) are becoming more pervasive in transportation, it is important to consider drivers’ perceptions of these vehicles. The existing research has investigated taking over AV control, its safety and acceptance. However, the preferences for self-driving in multiple traffic situations have not been extensively investigated. In Part I, we aim to bridge these gaps by investigating such preferences in high and low traffic complexities. Eighty-eight participants in North America were recruited. They viewed video recordings of driving in the city of Toronto, the regional municipality of Waterloo and highways to answer survey questions. Their responses regarding perceptions and preferences were simply analysed using descriptive statistics and Chi-square test at various traffic situations with two traffic complexities. It showed strong preferences for self-driving in most low complexity situations and certain situations in both complexities. These findings can suggest a few applicable design principles of AV decision system regarding traffic situation-based and biased perceptions-based user preferences. In Part II, we extend our analyses to user preferences for multiple two-stage actions of AVs and suggest additional design principles of the system with a more-in-depth insights.
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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 | 0.002 | 0.008 |
| 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 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".