Vehicular communications is expected to be one of the key applications for cellular networks during the following decades. Key international organizations have already described in detail a number of related use cases, along with their requirements. This article provides a comprehensive analysis of these use cases and a harmonized view of the requirements for the latest and most advanced autonomous driving applications. It also investigates the extent of support that 4G and 5G networks can offer to these use cases in terms of delay and spectrum needs. The paper identifies open issues and discusses trends and potential solutions.

As the automotive industry moves toward fully autonomous driving, the goal is to enable vehicles to operate safely without human control in all environments. Implementing Vehicle-to-Everything (V2X) communications in highway environments poses considerable challenges. Several critical services have strict network performance requirements as they deal with safety features. Existing fifth-generation (5G) base station schedulers do not discriminate among critical and non-critical automated driving functions. Therefore, in cases of increased traffic load, there is a significant drop in their performance, and, consequently, increased risk for accidents. Our paper discusses these issues and provides an adaptive scheduler called SOVANET+. The new scheduler acknowledges the Radio Access Network (RAN) load, and the requirements of critical, automated driving applications, together with channel quality, and optimizes the allocation of resources to critical services. The performance of SOVANET+ is evaluated through extensive simulations in the highway environment, an area less examined than urban scenarios. Results indicate that the adoption of SOVANET+ presents clear advantages to critical services compared to existing solutions.

Automated driving requires the support of critical communication services with strict performance requirements. Existing fifth-generation (5G) schedulers residing at the base stations are not optimized to differentiate between critical and non-critical automated driving applications. Thus, when the traffic load increases, there is a significant decrease in their performance. Our paper introduces SOVANET, a beyond 5G scheduler that considers the Radio Access Network (RAN) load, as well as the requirements of critical, automated driving applications and optimizes the allocation of resources to them compared to non-critical services. The proposed scheduler is evaluated through extensive simulations and compared to the typical Proportional Fair scheduler. Results show that SOVANET’s performance for critical services presents clear benefits.

5G New Radio Vehicle-to-Everything (NR V2X) networks must support heterogeneous traffic with strict and diverse latency requirements. Conventional proportional-fair (PF) scheduling does not explicitly account for packet deadlines, which can lead to deadline violations for critical vehicular services under congestion. This paper studies deadline-aware MAC scheduler-weight adaptation for 5G NR V2X using probabilistic prediction and reinforcement learning. We implement a closed-loop ns-3/5G-LENA framework in which network telemetry is exchanged with a Python control agent through ns3-ai shared memory. Gaussian Mixture Model (GMM), Hidden Markov Model (HMM), and Bayesian Logistic Regression (BLR) classifiers are used to predict imminent deadline violations. Their outputs are either mapped directly to scheduler weights or provided as additional state information to a Proximal Policy Optimization (PPO) agent. We evaluate ten scheduling strategies: PF, a non-learning Slack-Based Deadline-Aware Scheduler (SB-DAS), three classifier-only controllers, three classifier-assisted PPO variants, PPO-only, and PPO-only with safety shielding. Experiments are conducted across three vehicle densities and three random seeds per density, using the Deadline-Constrained Packet Reception Ratio (DC-PRR) as the main metric. The PF baseline achieves 61.55% mean DC-PRR and degrades from 75.2% at 30 vehicles to 44.1% at 60 vehicles. In contrast, all adaptive strategies exceed 95% mean DC-PRR and recover 34–38 percentage points over PF in every paired density/seed comparison. The main result is therefore the robust gap between PF and deadline-aware adaptation. Differences among the adaptive controllers are much smaller and fall within the observed seed-to-seed variability. In particular, SB-DAS, which uses no classifier, neural network, or training, achieves DC-PRR statistically indistinguishable from the learned and probabilistic controllers. This indicates that, in the evaluated scenarios, most of the gain comes from deadline awareness itself rather than from learning. We also find that adding classifier-derived violation probabilities to PPO does not consistently improve performance over PPO using raw telemetry alone. To support reproducibility and deployment assessment, the paper includes detailed parameter tables, reward-coefficient and sensitivity analysis, scheduler-weight sensitivity, and per-controller inference-latency and complexity measurements.

5G networks are expected to be a key enabler to support autonomous driving. During the past years, multiple use cases have been defined and several standardized solutions have been specified. Although significant breakthroughs have been achieved, it is still under investigation if existing solutions are able to fully support all requirements for all levels of autonomous driving automation. This paper provides a harmonized view of the requirements for the latest and most advanced autonomous driving use cases. It also investigates the extent of support 5G networks can offer to these use cases in terms of delay and spectrum. The paper identifies open issues and discusses trends and potential solutions that could tackle them.

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.