| Description: |
Despite significant advancements in AI and automated driving, a robust ethical framework for AV decision-making remains undeveloped. Such a framework requires clearly defined moral attributes to guide AVs in evaluating complex and ethically sensitive scenarios. Existing frameworks often rely on a single normative ethical theory, limiting their ability to address the nuanced nature of human decision-making and leading to conflicting outcomes. Augmented Utilitarianism (AU) offers a promising alternative by integrating elements of virtue ethics, deontology, and consequentialism into a non-normative framework. Grounded in moral psychology and neuroscience, AU employs mathematical ethical goal functions to capture societally aligned attributes. In this study, we propose and evaluate a method to elicit these attributes for AV decision-making. One hundred participants were presented with traffic scenarios, including critical and non-critical situations, and tasked with evaluating the relevance of an initial set of 11 attributes (e.g., physical harm, psychological harm, and moral responsibility) while suggesting additional relevant attributes. Results identified two new attributes—environmental harm and energy efficiency—and revealed that four attributes (physical harm, psychological harm, legality of the AV, and self-preservation) varied significantly between critical and non-critical scenarios. These findings suggest that the weight of attributes in ethical goal functions may need to adapt to situational criticality. The method was validated based on key evaluation criteria: it demonstrated sensitivity by producing varying relevance scores for attributes, was deemed relevant by participants for eliciting AV decision-making attributes, and allowed for the identification of additional attributes, enhancing the robustness of the framework. This work contributes to the development of a dynamic and context-sensitive ethical framework for AV decision-making. |