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The automotive industry stands at a transformative crossroads where artificial intelligence is fundamentally redefining how vehicles perceive, navigate, and interact with their environment. From ADAS engineering teams working on advanced driver-assistance features to autonomous systems integration specialists deploying full self-driving capabilities, the promise of intelligent mobility has captivated engineers, executives, and consumers alike. Yet the path from prototype to production-ready AI-driven systems is littered with costly missteps that have delayed launches, inflated R&D budgets, and eroded consumer trust. Understanding these pitfalls before they derail your program can mean the difference between leading the mobility revolution and becoming a cautionary tale in automotive AI deployment. Organizations racing to deliver AI-Driven Mobility solutions often repeat the same fundamental errors, despite ample evidence from predecessors who stumbled before them. These mistakes s...