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.

翻译：软件、硬件、计算和控制领域的最新进展推动了自主系统领域的重大进展,特别是自主机器应不断估计其移动和运行的情景如何在预先确定的时间框架内演变,并预见网络能否达到商定的服务质量(Qos),否则,应采取适当的对策满足应用要求。根据这些方针,我们在本文件中提出在自主系统中实现预测性Qos(PQos)的可能方法,并讨论哪些使用案例将特别受益于网络预测。 然后,我们阐述仍开放供今后研究的领域的难题。作为案例研究,我们证明机器学习是否能促进远程驱动式使用,作为不同测量信号的函数。