Examining the Adoption of Autonomous Vehicles in China, Considering Factors Related to Human Behavior, Automation, and the Environment
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Despite the growing popularity of autonomous vehicles (AVs), public acceptance of AV technologies remains uncertain. This study aims to explore how user demographics, human-related factors, and environmental factors influence people's decisions to adopt three distinct AV types: general AVs, shared AVs, and AVs with a human-shaped dummy driver. 765 valid responses were gathered via a questionnaire survey conducted in China. A random parameter univariate probit model with heterogeneity in means and a random parameter bivariate model with heterogeneity in means were employed. Findings suggest that gender, occupation, age, trust, self-efficacy, behavioral intentions, perceived safety risks as well as social and traditional media influences are the prominent factors affecting individuals' decision to adopt these AVs. Furthermore, this study reveals that significant factors vary depending on the type of AVs considered. These results are expected to offer insights for policymakers, promoters of AVs and transportation authority’s seeking to enhance public acceptance.
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| 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 | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it