Leveraging Generative AI and Cloud Computing for Enhanced Autonomous Vehicle Systems a Software-Defined Architecture Approach

The rapid evolution of autonomous vehicle (AV) systems requires scalable, adaptable, and intelligent software architectures to cater for high demands in security, reliability, and real-time processing. This paper introduces a novel software-defined architecture combining generative artificial intelligence (AI) with cloud computing for extending the performance and capabilities of AVs. The proposed methodology uses generative AI models for dynamic perception, route planning, and anomaly detection and is implemented on cloud computing infrastructure to lend orders of magnitude larger computational resources for scaling on-the-fly learning among distributed AV fleets. Decoupling hardware-specific features and transitioning toward a software-defined paradigm, the processing platform allows for quick updates, continuous learning, and flexible deployment of world-leading AI models. Experimental results and simulated scenarios show better situational awareness, response time, and system adaptability when compared to those of traditional architectures. This work outlines a promising pathway for the creation of future-proof, robust, intelligent AV ecosystems, enabled by the cooperation of generative AI and cloud computing systems.