The increasing presence of automated vehicles (AVs) has intensified discussions on optimal deployment strategies that ensure public safety, efficiency, and public acceptance. Although dedicated lanes have been proposed to improve safety and operational reliability, the current limited market presence of AVs renders this option economically and logistically unfeasible in the near term. This study used pooled and mixed-effects probit models using data from the Pew Research Center’s American Trends Panel (Wave 99, 2021) to examine public perceptions of AV acceptability under four deployment scenarios: dedicated AV lanes, driverless, mandatory crash reporting, and mixed traffic with a licensed driver present. Descriptive analysis revealed that labeling AVs and requiring crash reporting were generally perceived more favorably than the use of dedicated lanes. Model results indicated that AV deployment in mixed traffic was viewed as more acceptable when transparency-oriented measures, such as visible labeling and mandatory crash reporting, were incorporated. In contrast, requiring a licensed driver within AVs significantly reduced public acceptability compared to infrastructure-based options. These relationships remained robust across gender, AV familiarity, and comfort levels, while age, income, education, and political ideology exhibited differentiated effects across scenarios. The findings suggest that policymakers should prioritize cost-effective, transparency-enhancing strategies, such as AV labeling and crash reporting, over capital-intensive infrastructure interventions like dedicated lanes. In addition, reducing dependence on licensed drivers within AVs may facilitate broader public acceptance, particularly during early phases of deployment. These results provide actionable insights for developing AV deployment policies that balance technological innovation, fiscal feasibility, and public trust in the United States.
Mdimi et al. (2026) studied this question.