  Cooperative Localization is expected to play a crucial role in various
applications in the field of Connected and Autonomous vehicles (CAVs). Future
5G wireless systems are expected to enable cost-effective Vehicle-to-Everything
(V2X)systems, allowing CAVs to share with the other entities of the network the
data they collect and measure. Typical measurement models usually deployed for
this problem, are absolute position from Global Positioning System (GPS),
relative distance and azimuth angle to neighbouring vehicles, extracted from
Light Detection and Ranging (LIDAR) or Radio Detection and Ranging(RADAR)
sensors. In this paper, we provide a cooperative localization approach that
performs multi modal-fusion between the interconnected vehicles, by
representing a fleet of connected cars as an undirected graph, encoding each
vehicle position relative to its neighbouring vehicles. This method is based
on:i) the Laplacian Processing, a Graph Signal Processing tool that allows to
capture intrinsic geometry of the undirected graph of vehicles rather than
their absolute position on global coordinate system and ii) the temporal
coherence due to motion patterns of the moving vehicles.
