(untitled) (International Journal of Artificial Intelligence Engineering and Systems, 2026)
The proliferation of autonomous vehicles demands communication infrastructures capable of supporting extreme requirements for latency, reliability, and data throughput. Cooperative edge intelligence, realized through multi-access edge computing and distributed learning paradigms, and network slicing, as a softwarized 5G and beyond capability, together form a promising coalition to meet these demands. This paper presents a system-level investigation into the architectural integration of cooperative edge intelligence and network slicing for autonomous vehicle communication systems. It examines the structural trade-offs inherent in orchestrating computation and communication resources across multi-tier edge clouds while maintaining stringent service-level agreements through logical network partitions. The analysis extends to governance models for slice lifecycle management, resource isolation policies, and fairness mechanisms that address diverse stakeholder interests. Further, deployment challenges such as mobility-induced session continuity, energy efficiency, resilience under infrastructure degradation, and security vulnerabilities are critically evaluated. Policy and regulatory implications concerning spectrum management, data sovereignty, and inter-operator coordination are discussed in the context of ongoing standardization and real-world deployments. By synthesizing advances from distributed intelligence, softwarized networking, and autonomous driving ecosystems, the paper provides a forward-looking perspective on constructing robust, sustainable, and equitable vehicular communication systems. The discussion is firmly grounded in the relevant literature and draws cross-domain comparisons to highlight the unique constraints and opportunities of this converged infrastructure.