Investigating how the public acceptance of autonomous vehicles evolve with the changes in the level of knowledge: A demographic analysis
Bibliographic record
Abstract
Autonomous vehicles (AVs) are expected to provide various advantages such as improved mobility, increased comfort, and reduced traffic accidents. However, the deployment of AVs is contingent upon the public’s attitude, which in turn may affect their consequences. The relationship between public knowledge about AVs and attitude has been debated. While some studies indicate a positive correlation between knowledge and optimism, others suggest a negative association. The present study aims to evaluate the association between public knowledge, attitude, and AVs in the US. A questionnaire survey was conducted between June and November 2022, yielding 5778 complete responses from various regions of the US. Data were analyzed to estimate the public attitude and level of knowledge by region. Findings revealed a negative shift in public attitude towards AVs with increased knowledge. Specifically, a 1% increase in knowledge was associated with a 0.65% reduction in interest, a 0.68% decrease in trust, and a decline of $2466 USD in willingness to pay for AVs, as well as a 0.56% increase in concerns about traveling in AVs. In addition, further analysis was conducted to understand how the public attitude of different demographic groups evolves with the level of knowledge. Furthermore, the results of this study were discussed in light of the introduction of the automobiles showing a lot of similarities in the public attitude towards the introduction of both the automobiles (more than 100 years ago) and AVs (now). In addition, the results were discussed in light of the theory of diffusion of innovation and the Gartner Hype Cycle which are used to explain how the public reacts to innovations.
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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 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".