Road traffic crashes claim approximately 1.19 million lives annually worldwide, and human error accounts for the vast majority, yet the autonomous vehicle acceptance literature models adoption almost exclusively through technology-centered pull factors such as perceived usefulness and trust. This study examines a moderated mediation model in which perceived community driving-safety concern (PCSC) predicts evaluations of AI versus human driving capability, mediated by Generalized AI Orientation and moderated by personal driving frequency. Weighted structural equation modeling is applied to a nationally representative U.S. probability sample from Pew Research Center's American Trends Panel Wave 152, using Weighted Least Squares Mean and Variance Adjusted (WLSMV)-estimated confirmatory factor analysis on ordinal indicators, bias-corrected bootstrap inference, and seven robustness checks including Imai sensitivity analysis, E-value confounding thresholds, and propensity score matching. Results reveal a dual-pathway mechanism constituting an inconsistent mediation: PCSC exerts a small positive direct effect on AI driving evaluation, consistent with a domain-specific push interpretation, while simultaneously suppressing Generalized AI Orientation, which is itself a strong positive predictor of AI driving evaluation. Conditional indirect effects are negative and statistically significant at low, mean, and high levels of driving frequency. These findings establish a risk-spillover mechanism whereby community driving-safety concern promotes domain-specific AI endorsement yet suppresses domain-general AI enthusiasm, yielding a near-zero net total effect.

翻译：全球每年约有119万人死于道路交通事故，其中绝大多数由人为失误造成。然而，关于自动驾驶汽车接受度的文献几乎完全通过以技术为中心的拉动因素（如感知有用性和信任）来建模采纳行为。本研究考察了一个有调节的中介模型，其中感知社区驾驶安全担忧（PCSC）通过广义AI导向的中介作用和个人驾驶频率的调节作用，预测对AI与人类驾驶能力的评价。基于皮尤研究中心美国趋势小组第152波全国代表性概率样本，采用加权结构方程模型，对有序指标进行加权最小二乘均值和方差调整（WLSMV）估计的验证性因子分析、偏差校正自助法推断，以及包括Imai敏感性分析、E值混杂阈值和倾向得分匹配在内的七项稳健性检验。结果显示存在一个构成不一致中介的双路径机制：PCSC对AI驾驶评价产生微小的正向直接效应，符合特定领域推动解释，同时抑制广义AI导向，而后者本身是AI驾驶评价的强正预测因子。条件间接效应在低、中、高驾驶频率水平上均为负且统计显著。这些发现建立了一种风险溢出机制，即社区驾驶安全担忧促进了特定领域的AI认可，却抑制了跨领域的AI热情，导致近乎为零的净总效应。