Network slicing is a promising technique used in the smart delivery of traffic and can satisfy the requirements of specific applications or systems based on the features of the 5G network. To this end, an appropriate slice needs to be selected for each data flow to efficiently transmit data for different applications and heterogeneous requirements. To apply the slicing paradigm at the radio segment of a cellular network, this paper presents two approaches for dynamically classifying the traffic types of individual flows and transmitting them through a specific slice with an associated 5G quality-of-service identifier (5QI). Finally, using a 5G standalone (SA) experimental network solution, we apply the radio resource sharing configuration to prioritize traffic that is dispatched through the most suitable slice. The results demonstrate that the use of network slicing allows for higher efficiency and reliability for the most critical data in terms of packet loss or jitter.

Network slicing has emerged as a transformative enabler in 5 G networks, offering tailored communication services for diverse traffic types on shared network infrastructure. In the context of autonomous driving and smart mobility, the ability to dynamically prioritize and manage sensor data—ranging from high- bandwidth video streams to low-latency text and binary position and coordination messages—plays a pivotal role in ensuring safe and efficient operation. This paper proposes a dynamic mobile network slicing framework designed to analyse vehicular traffic and adapt slicing policies to optimize resource allocation for autonomous driving applications. By leveraging distributed and disaggregated 5 G network architectures, the proposed solution ensures seamless propagation of slicing policies across radio access networks (RAN) and core systems building end-to-end network slices. Experimental evaluations in scenarios such as Automated Guided Vehicle (AGV)-assisted operations in industrial environments demonstrate significant performance improvements, including a reduction in packet loss from 65% to 0% under congested network conditions. The results highlight the potential of dynamic slicing to enhance communication reliability and performance in autonomous driving ecosystems, supporting the seamless exchange of diverse sensor data types.