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.