Impact of Accidents Involving Autonomous Vehicles on the Perceived Benefits and Concerns
Bibliographic record
Abstract
Autonomous vehicles (AVs) or self-driving cars have the potential to offer a large number of benefits such as improving the mobility for people with limited modes of transportation and the reduction of the emissions, energy, travel time, and required fleet size to service the same population.Despite the enthusiastic speculation of AVs, little is known about the public attitude towards AVs and the factors that affect the public acceptance of this emerging technology.However, the public attitude is considered a main determinant for the success of any new technology.Over the last few years, AVs were involved in multiple accidents that attracted the media and news.While these accidents are expected to affect the public attitude and discourage people from adopting AVs, the impact of these accidents on the public attitude has been rarely discussed in the literature.Thus, this paper mainly focuses on understanding and quantifying the impact of AVs accidents on the general opinion, level of trust, level of concern in traveling in an AV, perceived benefits, and perceived concerns using an online questionnaire survey that was completed by 1987 respondents from the USA.The results show that AVs accidents negatively affected the public attitude towards AVs that the level of interest in owning an AV decreased by 25%.Additionally, these accidents have negatively affected the level of trust in AVs that most of the respondents will not allow their kids to travel in an AV by themselves.
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How this classification was reachedexpand
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 | 0.000 |
| 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) | 0.000 | 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 teacher head, 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".