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This paper considers the problem of cooperative lidar sensing in vehicular networks. We focus on the task of associating the vehicle-generated measurements by lidars to enable a cooperative detection of vulnerable road users. The considered measurements are the three-dimensional bounding boxes extracted from the lidar point cloud. Focusing on a centralized architecture which aggregates and processes all the sensing information, we design a graph formulation of the association problem and we propose a novel solution based on Message Passing Neural Networks (MPNNs). The method has the advantage of accurately learning the associations and the measurement statistics directly from data. We validate the proposed approach on a cooperative sensing scenario simulated by CARLA, an open-source high-fidelity simulator for automated driving scenarios. For the generation of bounding boxes related to …