Advanced Adaptive Scheduling for Autonomous Driving in Beyond-5G/6G Networks

The shift toward autonomous driving is steadily reducing the need for human intervention in vehicular operations, but sustaining this shift depends on 6G networks that can reliably multiplex diverse, delay-critical services under deterministic latency, throughput, and reliability constraints. Static over-provisioning can meet the stringent Quality of Service (QoS) requirements of critical traffic, but at the cost of resource starvation for co-existing non-critical services, and it degrades further under adverse channel conditions or network congestion where spectrum must be used efficiently. This motivates dynamic, service-aware scheduling as a core requirement for multi-service 6G vehicular architectures. This paper presents SOVANET+, an extended scheduling technique that jointly accounts for service criticality (critical vs. non-critical), network load, and wireless link quality to allocate resources adaptively across coexisting Vehicle-to-Everything (V2X) services. We evaluate SOVANET+ through extensive simulations of a congested single-cell urban-grid deployment supporting delay-critical automated driving services. Results show that SOVANET+ achieves lower latency and jitter, higher throughput, and improved uplink and downlink reliability compared to existing scheduling approaches, while scaling effectively to large numbers of connected autonomous vehicles, supporting its viability for next-generation intelligent transportation systems.