Future wireless network technology provides automobiles with the connectivity feature to consolidate the concept of vehicular networks that collaborate on conducting cooperative driving tasks. The full potential of connected vehicles, which promises road safety and quality driving experience, can be leveraged if machine learning models guarantee the robustness in performing core functions including localization and controls. Location awareness, in particular, lends itself to the deployment of location-specific services and the improvement of the operation performance. The 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 decentralized principle of vehicular localization reinforced by machine learning techniques in dense urban environments with frequent inaccessibility to reliable measurement. As such, the collaboration of multiple vehicles enhances the positioning performance of machine learning approaches. A virtual testbed is developed to validate this machine learning model for real-map vehicular networks. Numerical results demonstrate universal feasibility of cooperative localization, in particular, for dense urban area configurations.

翻译：未来无线网络技术为汽车提供连接功能，以强化协同驾驶任务中协作的车辆网络概念。若机器学习模型能保障定位与控制等核心功能的鲁棒性，则可充分发挥联网车辆在道路安全与优质驾驶体验方面的潜力。其中，位置感知尤其有利于部署位置特定服务并提升运行性能。定位需要直接与网络基础设施通信，随着网络规模扩大，这种集中式定位方案将变得难以处理。作为集中式方案的替代方案，本文探讨了在密集城市环境中（频繁难以获取可靠测量值时）由机器学习技术强化支持的车辆定位去中心化原理。多车协作提升了机器学习方法的定位性能。通过开发虚拟测试平台验证该机器学习模型在真实地图车辆网络中的表现。数值结果证明了协同定位的普遍适用性，尤其在密集城区配置下。