Distributed Dynamic Map Fusion via Federated Learning for Intelligent Networked Vehicles
This work addresses the problem of improving sensing capabilities for intelligent networked vehicles, but it appears incremental as it builds on existing federated learning and map fusion techniques.
The paper tackles dynamic map fusion for networked vehicles to enhance sensing range and accuracy, proposing a federated learning framework that achieves high map quality by addressing unknown object counts, uncertainties, and missing labels, with experimental verification in CARLA simulation showing superior performance and robustness.
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.