  Cellular vehicle-to-everything (C-V2X) communication is a key enabler for
future cooperative automated driving and safety-related applications. The
requirements they demand in terms of Quality of Service (QoS) performance vary
according to the use case. For instance, Day-1 applications such as Emergency
Brake Light warning have less strict requirements than remote driving. In this
paper, we seek to answer two questions: Are current LTE networks ready to
support Day-1 applications at all times? And, can underperforming situations be
reliably predicted based on GPS and network-related information? To address
these questions, we first implement a system that collects positioning data and
LTE key performance indicators (KPIs) with a higher time resolution than
commercial off-the-shelf LTE modems, while simultaneously measuring the
end-to-end (E2E) delay of an LTE network. We then use this system to assess the
readiness of multiple mobile network operators (MNOs) and a live Mobile Edge
Computing (MEC) deployment in an urban scenario. For evaluating whether an
adaptable operation is possible in adverse circumstances, e.g., by performing
hybrid networking or graceful degradation, we finally use Machine Learning to
generate a client-based QoS predictor and forecast the achievable QoS levels.
