The rapid transformation of automotive systems into highly autonomous, software-defined, and interconnected platforms has significantly challenged traditional paradigms of safety assurance and certification. This research investigates the evolution of safety and cybersecurity frameworks within the context of modern automotive and cyber-physical systems, emphasizing the need for open, adaptive, and model-driven approaches. Drawing upon a diverse body of literature encompassing safety certification challenges, regulatory frameworks such as ISO/SAE 21434 and United Nations cybersecurity regulations, and model-based architectural methodologies including EAST-ADL2, the study critically analyzes the limitations of conventional assurance methods. It further explores the integration of formal modeling techniques, automated safety analysis, and runtime fault tolerance mechanisms in addressing the complexity of autonomous vehicle systems. The methodology employs qualitative synthesis and case-oriented analytical frameworks to examine both theoretical and applied perspectives, including insights from software engineering case studies and focus group-based evaluations. The findings reveal that while existing standards provide a structured foundation for safety and cybersecurity, they struggle to accommodate the dynamic and evolving nature of software-defined vehicles. Model-driven development and automated compliance tools emerge as promising enablers of scalable and adaptive assurance processes. However, challenges persist in aligning regulatory requirements with real-time system behavior, ensuring interoperability across stakeholders, and maintaining assurance validity over continuous updates. The study concludes by proposing a holistic framework that integrates regulatory compliance, model-based engineering, and adaptive certification mechanisms to support the next generation of resilient automotive systems.

Safety assurance, automotive cybersecurity, model-driven development, autonomous vehicles

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