To ensure safety in teleoperated driving scenarios, communication between vehicles and remote drivers must satisfy strict latency and reliability requirements. In this context, Predictive Quality of Service (PQoS) was investigated as a tool to predict unanticipated degradation of the Quality of Service (QoS), and allow the network to react accordingly. In this work, we design a reinforcement learning (RL) agent to implement PQoS in vehicular networks. To do so, based on data gathered at the Radio Access Network (RAN) and/or the end vehicles, as well as QoS predictions, our framework is able to identify the optimal level of compression to send automotive data under low latency and reliability constraints. We consider different learning schemes, including centralized, fully-distributed, and federated learning. We demonstrate via ns-3 simulations that, while centralized learning generally outperforms any other solution, decentralized learning, and especially federated learning, offers a good trade-off between convergence time and reliability, with positive implications in terms of privacy and complexity.

翻译：为确保远程驾驶场景中的安全性，车辆与远程驾驶员之间的通信必须满足严格的延迟和可靠性要求。在此背景下，预测服务质量（PQoS）被研究作为预测服务质量（QoS）意外下降的工具，并允许网络做出相应反应。本文设计了一个强化学习（RL）智能体，用于在车联网中实现PQoS。为此，基于从无线接入网（RAN）和/或终端车辆收集的数据以及QoS预测，我们的框架能够识别在低延迟和可靠性约束下发送汽车数据的最优压缩级别。我们考虑了不同的学习方案，包括集中式、完全分布式和联邦学习。通过ns-3仿真证明，尽管集中式学习通常优于其他方案，但去中心化学习，尤其是联邦学习，在收敛时间和可靠性之间提供了良好的权衡，并在隐私和复杂性方面具有积极意义。