Navigating the Future: Predictive Modeling of Safety Perceptions and Adoption Readiness of Autonomous Vehicles for Public Safety Enhancement
Bibliographic record
Abstract
The proliferation of autonomous vehicles (AVs) promises a shift towards more efficient and potentially safer roadways. However, the integration of AVs into existing transportation networks raises critical safety concerns, especially for vulnerable road users such as pedestrians and bicyclists. This paper examines the factors influencing these users' safety perceptions of sharing roadways with AVs. Leveraging survey data from Bike Pittsburgh and applying multinomial logit models, we assess how exposure to AVs, regulatory attitudes, and incidents involving AVs shape public trust and perceived safety. The study reveals that increased familiarity with AV technology correlates positively with safety perceptions, despite the historical impact of high-profile AV accidents. It also finds that public approval of AV testing is rising, suggesting a growing acceptance of AVs. Our analysis indicates a need for nuanced, evidence-based public policies that address the concerns of active transportation users and incorporate their perspectives into the formulation of safety guidelines in the era of AVs. Ultimately, the study highlights the importance of fostering positive interactions between AVs and vulnerable road users to promote harmonious coexistence and acceptance.
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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 | 0.003 | 0.008 |
| 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 | 0.002 | 0.001 |
| Open science | 0.001 | 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".