The Self-Driving Dilemma adapts the classical trolley problem to the context of autonomous transportation. In these scenarios, moral agents must choose between two unsatisfying options, one of which is designed to achieve a utilitarian outcome (e.g., sparing the greater number of people). Given the growing interest in autonomous vehicles (AVs) and their implications for infrastructure, safety, and mobility, research in moral and traffic psychology has increasingly investigated individual attitudes toward AVs through the lens of moral dilemmas. Empirical studies reveal intriguing contrasts between moral evaluations of AVs' behavior and people's willingness to adopt the same technology, with utilitarian reasoning emerging as a central factor shaping attitudes toward AVs. However, despite sharing a common structure, "parallel" versions of AV dilemmas differ in several respects, such as the number of individuals involved, the type of action required (swerving or staying), or the legal permissibility of the behavior (e.g., crossing against a red light). These features influence two latent variables crucial to moral decision-making: the perceived plausibility of the utilitarian option in a given scenario, and the individual's specific and "stable" tendency to endorse utilitarian solutions in moral dilemmas—defined here as the Utilitarian Moral Threshold (UMT). The present contribution seeks to operationalize UMTs using a novel Bayesian estimation method of individual- and context-specific Belonging Thresholds (Nucci et al., 2021). By employing informative prior distributions derived from both human and large language model (LLM) responses to AV dilemmas, this approach estimates an individual-specific UMT weighted by the relative impact of different dilemma features. This method builds on the assumption of utilitarianism as a unidimensional construct. Overall, this framework may enrich the interpretation of moral judgments in dichotomous dilemmas and offer new parameters for understanding individual attitudes toward autonomous vehicles.