  Recent advances in software, hardware, computing and control have fueled
significant progress in the field of autonomous systems. Notably, autonomous
machines should continuously estimate how the scenario in which they move and
operate will evolve within a predefined time frame, and foresee whether or not
the network will be able to fulfill the agreed Quality of Service (QoS). If
not, appropriate countermeasures should be taken to satisfy the application
requirements. Along these lines, in this paper we present possible methods to
enable predictive QoS (PQoS) in autonomous systems, and discuss which use cases
will particularly benefit from network prediction. Then, we shed light on the
challenges in the field that are still open for future research. As a case
study, we demonstrate whether machine learning can facilitate PQoS in a
teleoperated-driving-like use case, as a function of different measurement
signals.
