Connected and autonomous vehicles (CAVs) can reduce human errors in traffic accidents, increase road efficiency, and execute various tasks ranging from delivery to smart city surveillance. Reaping these benefits requires CAVs to autonomously navigate to target destinations. To this end, each CAV's navigation controller must leverage the information collected by sensors and wireless systems for decision-making on longitudinal and lateral movements. However, enabling autonomous navigation for CAVs requires a convergent integration of communication, control, and learning systems. The goal of this article is to explicitly expose the challenges related to this convergence and propose solutions to address them in two major use cases: Uncoordinated and coordinated CAVs. In particular, challenges related to the navigation of uncoordinated CAVs include stable path tracking, robust control against cyber-physical attacks, and adaptive navigation controller design. Meanwhile, when multiple CAVs coordinate their movements during navigation, fundamental problems such as stable formation, fast collaborative learning, and distributed intrusion detection are analyzed. For both cases, solutions using the convergence of communication theory, control theory, and machine learning are proposed to enable effective and secure CAV navigation. Preliminary simulation results are provided to show the merits of proposed solutions.

翻译：网联自主车辆（CAVs）可减少交通事故中的人为失误、提升道路效率，并执行从物流配送到智慧城市监控等多样化任务。实现这些效益要求CAVs能够自主导航至目标地点。为此，每辆CAV的导航控制器必须利用传感器与无线系统采集的信息，对纵向和横向运动进行决策。然而，CAVs自主导航的实现需要通信、控制与学习系统的融合集成。本文旨在明确揭示这种融合带来的挑战，并在两个主要应用场景（非协同与协同CAVs）中提出应对方案。具体而言，非协同CAVs导航的挑战包括稳定路径跟踪、针对网络物理攻击的鲁棒控制以及自适应导航控制器设计。同时，当多辆CAVs在导航过程中协调运动时，需分析稳定编队、快速协作学习及分布式入侵检测等基础问题。针对这两种场景，本文提出融合通信理论、控制理论与机器学习的解决方案，以实现高效安全的CAV导航。初步仿真结果验证了所提方案的优势。