The rapid integration of machine learning into autonomous driving systems has fundamentally altered long-established assumptions about safety assurance, regulatory compliance, and public trust in automotive technologies. Unlike traditional rule-based automotive control systems, machine learning-enabled autonomous driving systems are adaptive, probabilistic, and context-sensitive, challenging both technical validation practices and regulatory frameworks that were designed for deterministic behavior. This research article offers an in-depth qualitative and normative analysis of how safety assurance, standards, and regulatory approaches interact in the governance of adaptive autonomous driving systems. Drawing strictly on established international standards, regulatory theory, and peer-reviewed legal and safety research, the study explores the structural tension between innovation and accountability, the role of standards as trust-building instruments, and the evolving relationship between rules-based and goals-based regulation. The methodology adopts a comprehensive interpretive analysis of regulatory typologies, international standardization frameworks, and qualitative insights from prior empirical studies, synthesizing them into an integrated conceptual model of autonomous vehicle governance. The results reveal that existing standards and regulatory approaches provide partial but insufficient mechanisms to address learning-enabled behavior, particularly in post-deployment adaptation and system evolution. The discussion elaborates on the implications for institutional trust, liability allocation, and safety culture, while identifying critical limitations in current assurance practices. The article concludes that a hybrid governance approach—combining enforceable standards, adaptive assurance arguments, and continuous oversight—is essential for the safe and socially legitimate deployment of machine learning-based autonomous driving systems.

Autonomous driving systems, machine learning safety, regulatory governance, safety assurance

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