Public attitude towards autonomous vehicles before and after crashes: A detailed analysis based on the demographic characteristics
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
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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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 | 0.001 |
| 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) | 0.006 | 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