Machine Learning-Aided Cooperative Localization under A Dense Urban Environment: Demonstrates Universal Feasibility

Abstract

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.