  The technology of dynamic map fusion among networked vehicles has been
developed to enlarge sensing ranges and improve sensing accuracies for
individual vehicles. This paper proposes a federated learning (FL) based
dynamic map fusion framework to achieve high map quality despite unknown
numbers of objects in fields of view (FoVs), various sensing and model
uncertainties, and missing data labels for online learning. The novelty of this
work is threefold: (1) developing a three-stage fusion scheme to predict the
number of objects effectively and to fuse multiple local maps with fidelity
scores; (2) developing an FL algorithm which fine-tunes feature models (i.e.,
representation learning networks for feature extraction) distributively by
aggregating model parameters; (3) developing a knowledge distillation method to
generate FL training labels when data labels are unavailable. The proposed
framework is implemented in the Car Learning to Act (CARLA) simulation
platform. Extensive experimental results are provided to verify the superior
performance and robustness of the developed map fusion and FL schemes.
