Exploring the Correlation Between Preexisting Knowledge and Public Perception of Self-Driving Cars
Bibliographic record
Abstract
Self-driving vehicles (SDVs) possess the potential to provide novel benefits while also presenting new risks. Consequently, SDVs are expected to not only influence the transportation network but also reshape urban landscapes, markets, economies, and public behavior. The public's willingness to utilize or ride in SDVs is a critical factor determining the extent to which their implications can be realized. Previous research has indicated that awareness of SDVs is a key factor influencing the public's decision-making and attitude toward this nascent technology. However, none of these studies have exclusively examined the relationship between the public's level of knowledge about SDVs and their attitudes. Thus, this study employs a questionnaire survey to investigate the relationship between the public's attitudes and their knowledge of SDVs. The study analyzes 2447 complete responses collected from participants in the United States. The findings suggest that individuals possessing prior knowledge of SDVs are more likely to use them. However, participants with intermediate knowledge were the most likely to use SDVs compared to those with no knowledge and those with extensive knowledge. Moreover, the analysis demonstrates that the relationship between the level of knowledge and acceptance of SDVs is non-linear and peaks at the intermediate knowledge level.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.017 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".