Impact of Aging on Driver Preferences for Self-Driving Modes and Behaviors in Two Traffic Complexities
Bibliographic record
Abstract
This study investigates age-related distinctions in preferences for self-driving vehicles, exploring their connections with traffic-related elements and individual perceptions. Analyzing two groups (23-44 and 60+ years old), the research uncovers nuanced findings that offer valuable insights for designing driver preference-based autonomous driving. The elder (Old: 60+) group, despite displaying elevated trust levels, exhibits lower preferences for self-driving compared to the young-to-middle (Y-M: 23-44) aged group. This discrepancy is highlighted alongside a significant significance between perceived difficulty and self-driving preferences in both age groups. At each traffic situation, the elder group lacks statistical significances between traffic complexity and perceived difficulty, signaling more intricate traffic perception process. Correspondence analysis underscores age-specific preferences for extreme human-engaged or -disengaged actions, handing over a control and no informing, emphasizing significances of situation-specific considerations. Correlations between current trust and preference choices align in both groups. As a result, we suggest various design considerations that could potentially improve driver-preference factor; customizable actions, gradual automation transitions, factor-specific scaling, and specific cut-off threshold, etc. This study not only reveals age-related variations but also provides potential design principles for preference-based decision-making systems in autonomous vehicles (AVs).
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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 | 0.002 |
| 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 | 0.001 |
| 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".