ST: 4. SD: Car Function manufacturer Remote cloud Infrastructure Infrastructure layer manager controller Core network Figure 13.4 Network slicing architecture and slice instantiation for autonomous driving services. Source: Adapted from [20]. 13 5G Network Slicing for V2X Communications: Technologies and Enablers Figure 13.4 also graphically sketches the instantiation of a slice dedicated to autonomous driving. It relies on V2V as the prevalent RAT connection mode, and on additional RAN/CN functions, e.g. for network-controlled resource allocation over the PC5 interface (in the eNodeB), mobility (in the AMF), authentication, authorization, and subscription manage- ment (in the AUSF). Additionally, ultralow latency and highly reliable V2N connectivity with an edge-based V2X AS are required for video/data exchange that helps vehicles in high-definition map processing of the surrounding area and to extend the visual perception of each vehicle. Departing from a mere theoretical analysis, the work in [16] focuses on resource allocation in the RAN segment for two types of slices to be served in the same cell, i.e. eMBB requested by conventional UEs and V2X slices by VUEs. A strategy based on offline Q-learning is proposed to ensure the adequate splitting of resources between the two slices, to meet their demands in terms of throughput (for the eMBB slice) and latency (for the V2X slice). Different network slices need to be devised for different V2X services. Starting from this concept, in [15] the authors address the allocation of radio resources in the downlink direc- tion for the infotainment and autonomous driving slices instantiated by VUEs. The former is supported through V2I links, whereas the latter can also rely on V2V communications, with some vehicles acting as relays for their neighbors to improve connectivity. The work in [17] leverages the Euclidean norm theory for proposing a reliability and latency joint function to evaluate the impact on the performance. A network slicing scheme is designed to meet the 