Examining the Adoption of Autonomous Vehicles in China, Considering Factors Related to Human Behavior, Automation, and the Environment
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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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 | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| 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".