Predictive QoS for Tele-Operated Driving over 5G SA Networks: an Experimental Study

Vehicular connectivity is becoming an integral part of the automotive industry, pushed forth by edge computing and smart cities. However, in urban environments, wireless links are prone to physical disturbances. To maintain their service with these conditions, vehicular networking architectures need the ability to anticipate phenomena and adapt proactively. The multitude of variables involved in this problem calls for deep learning approaches. As such, predictive Quality-of-Service has shown promising contributions to the performance of low-latency infrastructures, but a field study of appropriate recurrent neural networks has yet to be conducted for vehicular networks. Moreover, the inherent heterogeneity of urban networks can be leveraged for service continuity purposes, by developing a radio access technology selection algorithm based on the predictions made for each technology. In that regard, while standalone 5G has been publicized as a key technology for very demanding applications like tele-operated driving, previous works have not studied its contribution in a real-life environment. This work thereby proposes a predictive Quality-of-Service infrastructure with proactive radio access technology selection for urban vehicular networks, evaluated by field trial and involving 5G SA, C-V2X and ITS-G5. Experimental observations show the enhanced contribution of 5G SA, with a minimal latency of 16 milliseconds and higher overall reliability than device-to-device communications. These measurements pave the way to cellular vehicular communications in applications with high service level requirements. Moreover, our system is able to ensure 99.9% success rates from 72% of the vehicle travel time to more than 90% of the time. Average reliability increases from 95.7% to 99.4%.