Ethical Architectures and Decision Logics in Sustainable Autonomous Transportation: Normative Foundations, Technical Pathways, and Societal Implications

Abstract

The rapid development and deployment of autonomous vehicles has transformed road transportation from a predominantly human-centered activity into a complex socio-technical system governed by algorithms, sensors, and learning architectures. This transformation has intensified long-standing ethical questions surrounding road safety, responsibility, harm distribution, and social welfare, while simultaneously introducing novel dilemmas related to machine decision-making, sustainability, and moral accountability. Within this context, ethical decision-making frameworks for autonomous transportation have emerged as a central scholarly and policy concern, particularly in relation to the contrast between rule-based systems grounded in explicit normative prescriptions and learning-based systems driven by data-intensive adaptive models. This article offers an extensive, theory-driven, and critically engaged analysis of ethical decision-making in sustainable autonomous transportation, with particular attention to the comparative strengths, limitations, and societal implications of rule-based and learning-based approaches. Drawing on interdisciplinary literature spanning moral philosophy, criminal law theory, artificial intelligence ethics, traffic safety research, and engineering studies, the article situates autonomous vehicle ethics within broader debates on utilitarianism, deontological constraints, responsibility attribution, and the ethics of risk. The analysis is anchored by recent comparative scholarship examining ethical decision-making in sustainable autonomous transportation, which highlights the tensions between transparency, predictability, adaptability, and moral pluralism in algorithmic systems (Ethical Decision-Making In Sustainable Autonomous Transportation: A Comparative Study Of Rule-Based And Learning-Based Systems, 2025). Through a qualitative methodological framework that synthesizes normative analysis with interpretive evaluation of empirical and experimental findings, the study elucidates how ethical principles are operationalized, contested, and transformed within autonomous driving architectures. The results emphasize that ethical decision-making in autonomous transportation cannot be reduced to technical optimization problems, but must instead be understood as an evolving moral practice shaped by legal norms, cultural expectations, sustainability goals, and public trust. The discussion advances a critical argument for hybrid ethical architectures that integrate rule-based constraints with learning-based adaptability, while acknowledging persistent limitations related to moral disagreement, data bias, and institutional governance. By offering a comprehensive and deeply elaborated account of ethical decision-making in autonomous transportation, this article contributes to ongoing scholarly efforts to align technological innovation with societal values, environmental sustainability, and the ethical demands of contemporary mobility systems.