Teleoperated driving (TD) is envisioned as a key application of future sixth generation (6G) networks. In this paradigm, connected vehicles transmit sensor-perception data to a remote (software) driver, which returns driving control commands to enhance traffic efficiency and road safety. This scenario imposes to maintain reliable and low-latency communication between the vehicle and the remote driver. To this aim, a promising solution is Predictive Quality of Service (PQoS), which provides mechanisms to estimate possible Quality of Service (QoS) degradation, and trigger timely network corrective actions accordingly. In particular, Reinforcement Learning (RL) agents can be trained to identify the optimal PQoS configuration. In this paper, we develop and implement two integrated RL agents that jointly determine (i) the optimal compression configuration for TD sensor data to balance the trade-off between transmission efficiency and data quality, and (ii) the optimal scheduling configuration to minimize the end-to-end latency by allocating radio resources according to different priority levels. We prove via full-stack ns-3 simulations that our integrated agents can deliver superior performance than any standalone model that only optimizes either compression or scheduling, especially in constrained or congested networks. While these agents can be deployed using either centralized or decentralized learning, we further propose a new meta-learning agent that dynamically selects the most appropriate strategy between the two based on current network conditions and application requirements.

翻译：远程驾驶（TD）被视为未来第六代（6G）网络的关键应用。在该范式中，联网车辆将传感器感知数据传输至远程（软件）驾驶员，远程驾驶员返回驾驶控制指令以提升交通效率与道路安全。该场景要求车辆与远程驾驶员之间保持可靠且低延迟的通信。为此，预测服务质量（PQoS）成为一种有前景的解决方案，它提供机制以预估服务质量（QoS）的潜在退化，并据此及时触发网络纠正措施。特别地，强化学习（RL）智能体可被训练以识别最优PQoS配置。本文开发并实现了两个集成式RL智能体，它们联合确定：（i）TD传感器数据的最优压缩配置，以平衡传输效率与数据质量之间的权衡；（ii）最优调度配置，通过根据不同的优先级分配无线资源以最小化端到端延迟。我们通过全栈ns-3仿真证明，与仅优化压缩或调度的独立模型相比，我们的集成智能体能在受限或拥塞网络中实现更优性能。尽管这些智能体可通过集中式或分布式学习部署，我们进一步提出一种新的元学习智能体，它能根据当前网络条件与应用需求，动态选择两者中最合适的策略。