  Reliable wireless communications play an important role in the automotive
industry as it helps to enhance current use cases and enable new ones such as
connected autonomous driving, platooning, cooperative maneuvering, teleoperated
driving, and smart navigation. These and other use cases often rely on specific
quality of service (QoS) levels for communication. Recently, the area of
predictive quality of service (QoS) has received a great deal of attention as a
key enabler to forecast communication quality well enough in advance. However,
predicting QoS in a reliable manner is a notoriously difficult task. In this
paper, we evaluate ML tree-ensemble methods to predict QoS in the range of
minutes with data collected from a cellular test network. We discuss radio
environment characteristics and we showcase how these can be used to improve ML
performance and further support the uptake of ML in commercial networks.
Specifically, we use the correlations of the measurements coming from the radio
environment by including information of prior vehicles to enhance the
prediction of the target vehicles. Moreover, we are extending prior art by
showing how longer prediction horizons can be supported.
