Future wireless network technology will provide automobiles with a connectivity feature to consolidate the concept of vehicular networks that collaborate in conducting cooperative driving tasks. The full potential of connected vehicles, which promises road safety and a quality driving experience, can be leveraged if machine learning (ML) models guarantee robustness in performing core functions, including localization and controls. Location awareness, in particular, lends itself to the deployment of location-specific services and improvement of the operation performance. Localization entails direct communication to the network infrastructure, and the resulting centralized positioning solutions readily become intractable as the network scales up. As an alternative to the centralized solutions, this article addresses a decentralized principle of vehicular localization reinforced by ML techniques in dense urban environments with frequent inaccessibility to reliable measurement. As such, the collaboration of multiple vehicles enhances the positioning performance of ML approaches. A virtual testbed is developed to validate this ML model for real-map vehicular networks. Numerical results demonstrate the universal feasibility of cooperative localization, in particular, for dense urban area configurations.

中文翻译：

---

密集城市环境下的机器学习辅助协同定位：展现普遍可行性


未来的无线网络技术将为汽车提供连接功能，以巩固车辆网络协同执行协作驾驶任务的概念。如果机器学习 (ML) 模型保证执行定位和控制等核心功能的稳健性，则可以充分利用联网车辆的全部潜力，从而保证道路安全和优质驾驶体验。位置感知尤其有助于部署特定位置的服务和提高运营绩效。本地化需要与网络基础设施进行直接通信，随着网络规模的扩大，由此产生的集中式定位解决方案很容易变得棘手。作为集中式解决方案的替代方案，本文讨论了在经常无法获得可靠测量的密集城市环境中通过机器学习技术强化的车辆定位分散原则。因此，多辆车的协作增强了机器学习方法的定位性能。开发了一个虚拟测试台来验证真实地图车辆网络的机器学习模型。数值结果证明了合作定位的普遍可行性，特别是对于密集的城市区域配置。