Semi-Interpenetrating Cooperative Localization in Connected Vehicle Networks

TL;DR

This paper introduces a distributed cooperative localization method for connected vehicles that enhances accuracy and robustness by fusing GNSS data through a novel linear dynamical system model and optimization framework.

Contribution

It presents a new fusion mechanism and a distributed optimization framework for cooperative map matching in vehicular networks, improving localization performance.

Findings

Outperforms centralized and random fusion methods in simulations

Enhances robustness and accuracy of vehicle localization

Validates effectiveness with realistic vehicular network data

Abstract

We proposed a fusion mechanism for the distributed cooperative map matching (CMM) within the vehicular ad-hoc network. This mechanism makes the information from each node reachable within the network by other nodes without direct communication, thus improving the overall localization accuracy and robustness. Each node runs a Rao-Blackwellized particle filter (RBPF) that processes the Global Navigation Satellite System (GNSS) measurements of its own and its neighbors, followed by a map matching step that reduces or eliminates the GNSS atmospheric error. Then each node fuses its own filtered results with those from its neighbors for a better estimation. In this work, the complicated dynamics and fusion mechanics of these RBPFs are represented by a linear dynamical system. We proposed a distributed optimization framework that explores the model to improve both robustness and accuracy of the distributed CMM. The effectiveness of this distributed optimization framework is illustrated by simulation results on realistic vehicular networks drawn from data, compared with the centralized one and a decentralized one with random fusion weights.