Public attitude towards autonomous vehicles before and after crashes: A detailed analysis based on the demographic characteristics
Bibliographic record
Abstract
Autonomous vehicles (AVs) have the potential to offer a large number of benefits such as reducing the energy consumed and reducing the anxiety of the drivers. On the other side, the degree to which AVs will be adopted mainly depends on the public attitude and acceptance of this emerging technology. Over the last few years, AVs got involved in multiple accidents with different levels of severity. These accidents were widely covered in the media, creating a debate about the safety of this technology and discouraging people from adopting this new technology even if it offers a safer environment. In this study, a questionnaire survey was conducted to understand the impact of accidents involving AVs on the public perception of this technology for respondents with different demographic characteristics (age, gender, education, income, and prior knowledge about AVs). The results show the most negative shift in the attitude occurs for respondents who are older, female, and have no prior knowledge about AVs or their incidents. Additionally, the results shed light on the importance of educating the public about AVs in order to guarantee the highest level of acceptance. Finally, the findings of this paper can help AVs developer, policymakers, and transport planning agencies in understating the public attitude after accidents in order to react properly to avoid discouraging people from adopting 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 | 0.003 |
| 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 | 0.000 | 0.000 |
| 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".