  Accurate positioning is known to be a fundamental requirement for the
deployment of Connected Automated Vehicles (CAVs). To meet this need, a new
emerging trend is represented by cooperative methods where vehicles fuse
information coming from navigation and imaging sensors via
Vehicle-to-Everything (V2X) communications for joint positioning and
environmental perception. In line with this trend, this paper proposes a novel
data-driven cooperative sensing framework, termed Cooperative LiDAR Sensing
with Message Passing Neural Network (CLS-MPNN), where spatially-distributed
vehicles collaborate in perceiving the environment via LiDAR sensors. Vehicles
process their LiDAR point clouds using a Deep Neural Network (DNN), namely a 3D
object detector, to identify and localize possible static objects present in
the driving environment. Data are then aggregated by a centralized
infrastructure that performs Data Association (DA) using a Message Passing
Neural Network (MPNN) and runs the Implicit Cooperative Positioning (ICP)
algorithm. The proposed approach is evaluated using two realistic driving
scenarios generated by a high-fidelity automated driving simulator. The results
show that CLS-MPNN outperforms a conventional non-cooperative localization
algorithm based on Global Navigation Satellite System (GNSS) and a
state-of-the-art cooperative Simultaneous Localization and Mapping (SLAM)
method while approaching the performances of an oracle system with ideal
sensing and perfect association.
