Adaptive AI-Driven Digital Twin Frameworks for Autonomous Vehicles

Artificial Intelligence (AI) and Machine Learning (ML) are transforming Digital Twin (DT) technology into a real-time, intelligent ecosystem for Autonomous Vehicles (AVs). This chapter explores how AI-driven DT frameworks integrate sensor fusion, reinforcement learning, and edge-cloud collaboration to achieve continuous synchronization between physical and virtual systems. These smart twins enable predictive maintenance, adaptive decision-making, and safety validation with minimal latency. Industrial examples such as Tesla's Predictive Maintenance Twin and Audi's Virtual Prototyping Platform demonstrate practical implementations that enhance reliability, design accuracy, and efficiency. The chapter also discusses challenges in scalability, interoperability, and ethical governance, while highlighting emerging directions like quantum and federated learning. By combining intelligence, automation, and ethical design, AI-empowered Digital Twins form the foundation of next-generation, self-evolving, and resilient autonomous mobility systems.

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