Patterns Everywhere, Context Nowhere: Decidability and the Semantic Crisis in Autonomous Systems

Modern automotive engineering relies on a triad of standards to guarantee safety and quality: ASPICE for process maturity, ISO 26262 for functional safety against technical failures, and ISO 21448 (SOTIF) for mitigating operational hazards. However, as the industry transitions toward higher levels of automation driven by Deep Neural Networks (DNNs), these frameworks encounter a fundamental epistemological crisis: the problem of decidability. Operating purely within formal, syntactical domains, classic Turing machines lack semantic comprehension, remaining executionally context-free. Conversely, empirical DNNs act as inductive pattern-mating engines. This paper introduces the thesis "Patterns are Everywhere" to articulate how structural superpositions, scale-dependent blur, and the absence of Gregory Bateson's meta-structures render open-world data inherently undecidable for autarkic data-processing architectures. We challenge the traditional limits of Popperian falsificationism by introducing the Principle of Fractal Under-Complexity, demonstrating how the dynamic, structured nature of the physical world bounds real-world state-spaces. Finally, we argue that current automotive standards merely manage the symptoms of this semantic deficit and propose a paradigm shift toward dynamic, context-driven neuro-symbolic validation frameworks.