Deep Neural Networks for Cooperative Lidar Localization in Vehicular Networks

The exchange of sensing information through Vehicle-to-Everything (V2X) communications enables the development of cooperative systems for localization augmentation in connected automated vehicles. In V2X scenarios, the integration of measurements from multiple vehicles enhances the environmental perception which is of the utmost importance for enhanced safety services. In this paper, we propose a Deep Neural Network (DNN)-assisted cooperative localization method that relies on a centralized road infrastructure and a network of lidar sensors at vehicles. The proposed algorithm is referred to as DNN Implicit Cooperative Positioning (DNN-ICP) and performs two tasks. At first, each vehicle processes its lidar point cloud by a 3D object detector to identify static objects in the surrounding. Then, the estimated objects are collected at the road infrastructure which uses the aggregated information to improve the localization. Numerical results in a realistic vehicular scenario are presented to quantify the improvement provided by DNNICP with respect to a non-cooperative vehicle positioning scheme, showing the reduction of uncertainty on vehicle positioning.