5G Enabled Cooperative Localization of Connected and Semi-Autonomous Vehicles via Sparse Laplacian Processing

Abstract

Cooperative Localization has received extensive interest from several scientific communities including Robotics, Optimization, Signal Processing and Wireless Communications. It is expected to become a major aspect for a number of crucial applications in the field of Connected and (Semi-) Autonomous vehicles (CAVs), such as collision avoidance/warning, cooperative adaptive cruise control, safely navigation, etc. 5G mobile networks will be the key to providing connectivity for vehicle to everything (V2X) communications, allowing CAVs to share with other entities of the network the data they collect and measure. Typical measurement models usually deployed for this problem, are absolute position information from Global Positioning System (GPS), relative distance to neighbouring vehicles and relative angle or azimuth angle, from Light Detection and Ranging (LIDAR) or Radio Detection and Ranging (RADAR) sensors. In this paper, we provide a cooperative estimation approach that performs multi modal-fusion between interconnected vehicles. This method is based on a Graph Signal Processing tool, known as Laplacian Graph Processing, and significantly outperforms existing method both in terms of attained accuracy and computational complexity.