Federated Data-Driven Kalman Filtering for State Estimation

TL;DR

This paper introduces FedKalmanNet, a federated learning approach for vehicle localization that improves accuracy without relying on real-time V2X communication, demonstrated through extensive CARLA simulations.

Contribution

It reformulates KalmanNet within a federated learning framework for distributed vehicle localization, enhancing accuracy and reducing communication needs.

Findings

FedKalmanNet outperforms state-of-the-art methods in localization accuracy.

The approach eliminates the need for real-time V2X communication.

Experimental results validate the effectiveness of federated training for autonomous vehicle localization.

Abstract

This paper proposes a novel localization framework based on collaborative training or federated learning paradigm, for highly accurate localization of autonomous vehicles. More specifically, we build on the standard approach of KalmanNet, a recurrent neural network aiming to estimate the underlying system uncertainty of traditional Extended Kalman Filtering, and reformulate it by the adapt-then-combine concept to FedKalmanNet. The latter is trained in a distributed manner by a group of vehicles (or clients), with local training datasets consisting of vehicular location and velocity measurements, through a global server aggregation operation. The FedKalmanNet is then used by each vehicle to localize itself, by estimating the associated system uncertainty matrices (i.e, Kalman gain). Our aim is to actually demonstrate the benefits of collaborative training for state estimation in autonomous driving, over collaborative decision-making which requires rich V2X communication resources for measurement exchange and sensor fusion under real-time constraints. An extensive experimental and evaluation study conducted in CARLA autonomous driving simulator highlights the superior performance of FedKalmanNet over state-of-the-art collaborative decision-making approaches, in localizing vehicles without the need of real-time V2X communication.