## 1 Introduction
These technologies promise to enhance the reliability and availability of vehicular services in 5G VANETs. However, managing virtualised and distributed resources in 5G VANETs remains complex, and recent studies often rely on mathematical models and simulation platforms that do not support 5G implementation frameworks [8; 9]. This gap hinders the implementation of QoS and resource management solutions that are tailored to the stringent requirements of critical 5G VANETs applications. To address this gap, this paper investigates the following research question: How can the integration of SDN and NFV in connected vehicles enhance URLLC-class guarantees and resource management for critical vehicular data in 5G VANETs? We address this through the design and implementation of a novel SDN-Enabled 5G VANET framework that extends computing and storage capabilities to connected vehicles via VNFs, transforming them into transport nodes with embedded network management capabilities. This approach is powered by the open network interfaces of new architectural paradigms like 5G VANETs [10]. The integration of SDN and NFV capabilities in 5G VANETs connected vehicles allows for the pre-processing of critical vehicular data on vehicles to optimise QoS and resource utilisation at the edge. This differs from traditional VANET frameworks, where data processing begins at the Mobile Edge Computing (MEC) layer [11]. The proposed framework leverages SDN principles, including NFV, real-time traffic engineering, and network slicing, to ensure the efficient and reliable management of time-sensitive vehicular applications, promoting safer autonomous driving systems. The contributions of this paper include the following:  
* Novel SDN-Enabled Vehicular Edge Computing Framework: We embed a lightweight SDN gateway and Virtual Extensible Local-Area Network (VXLAN) underlay transport link in every vehicle, turning them into \"Software-Defined Vehicles (SDVs)\", capable of local flow classification, slice selection, on-board caching, and reliable data transmission. * URLLC-class Guarantees: We demonstrate the ability of the proposed framework to support Ultra-Reliable Low-Latency Communication for the 5G VANET applications responsible for critical vehicular services; this is validated through the 100% packet delivery rate for connected vehicles and 187% increase in flow rates over time. * Slice-Aware Resource Management: We propose a resource allocation scheme based on network slicing to enhance radio resource management at the vehicular edge. Our proposed scheme utilises the SDN underlay network to effectively classify critical vehicular data (Camera, LiDAR, and RADAR) into three different QoS classes for dynamic offloading to edge application servers. The remainder of this paper is organised as follows: Section 2 presents a review of related work. Section 3 outlines the proposed framework and implementation process, including software and hardware requirements, algorithms, and a flow diagram. Section 4 discusses the results, while Section 5 provides a performance evaluation and comparative analysis with existing solutions. Lastly, Section 6 concludes the paper with a summary of the findings, the significance of our study and its limitations, and directions for future work.