Public attitudes toward artificial intelligence (AI) and driving safety are typically studied in isolation using variable-centered methods that assume population homogeneity, yet risk perception theory predicts that these evaluations covary within individuals as expressions of underlying worldviews. This study identifies latent profiles of AI risk perception among U.S. adults and tests whether these profiles are differentially associated with community driving safety concerns. Latent class analysis was applied to nine AI risk-perception indicators from a nationally representative survey (Pew Research Center American Trends Panel Wave 152, n = 5,255); Bolck-Croon-Hagenaars corrected distal outcome analysis tested class differences on nine driving-safety outcomes, and survey-weighted multinomial logistic regression identified demographic and ideological predictors of class membership. Four classes emerged: Moderate Skeptics (17.5%), Concerned Pragmatists (42.8%), AI Ambivalent (10.6%), and Extreme Alarm (29.1%), with all nine driving-safety outcomes significantly differentiated across classes. Higher AI concern mapped monotonically onto greater perceived driving-hazard severity; the exception, comparative evaluation of AI versus human driving, was driven by trust rather than concern level. The cross-domain covariation provides person-level evidence for the worldview-based risk structuring posited by Cultural Theory of Risk and yields a four-class segmentation framework for AV communication that links AI risk orientation to transportation safety attitudes.

翻译：公众对人工智能（AI）及驾驶安全的态度通常采用假设群体同质性的变量中心方法独立研究，然而风险感知理论预测这些评估作为深层世界观的表达会在个体内部共同变化。本研究识别美国成年人中AI风险感知的潜在剖面，并检验这些剖面是否与社区驾驶安全关注度存在差异关联。基于具有全国代表性的调查（皮尤研究中心美国趋势小组第152期，样本量n=5,255）中9项AI风险感知指标进行潜在类别分析；采用Bolck-Croon-Hagenaars校正远端结局分析检验9项驾驶安全结局的类别差异，并通过调查加权多项逻辑回归识别类别归属的人口学及意识形态预测因子。共提取四类群体：温和怀疑者（17.5%）、关切务实者（42.8%）、AI矛盾者（10.6%）及极端警觉者（29.1%），所有9项驾驶安全结局均呈现显著类别差异。更高的AI关注度单调映射到更强烈的驾驶危险严重性认知；但AI与人类驾驶的比较评估这一例外情况主要由信任而非关注水平驱动。跨领域共变现象为风险文化理论所主张的基于世界观的风险结构框架提供了个体层面证据，并构建了连接AI风险倾向与交通安全态度的四类自动驾驶沟通细分框架。