Improving Localization Accuracy in Connected Vehicle Networks Using Rao-Blackwellized Particle Filters: Theory, Simulations, and Experiments

TL;DR

This paper presents a Rao-Blackwellized particle filter-based method for improving vehicle localization accuracy in connected vehicle networks, achieving sub-meter precision by mitigating GNSS biases through simulations and real-world experiments.

Contribution

The paper introduces a novel RBPF approach that jointly estimates common GNSS biases and vehicle positions, enhancing localization accuracy without expensive infrastructure.

Findings

Localization error reduced to below 1 meter in open sky conditions

The RBPF method effectively mitigates multipath and atmospheric biases

Experimental results validate simulation findings

Abstract

A crucial function for automated vehicle technologies is accurate localization. Lane-level accuracy is not readily available from low-cost Global Navigation Satellite System (GNSS) receivers because of factors such as multipath error and atmospheric bias. Approaches such as Differential GNSS can improve localization accuracy, but usually require investment in expensive base stations. Connected vehicle technologies provide an alternative approach to improving the localization accuracy. It will be shown in this paper that localization accuracy can be enhanced using crude GNSS measurements from a group of connected vehicles, by matching their locations to a digital map. A Rao-Blackwellized particle filter (RBPF) is used to jointly estimate the common biases of the pseudo-ranges and the vehicle positions. Multipath biases, which introduce receiver-specific (non-common) error, are mitigated by a multi-hypothesis detection-rejection approach. The temporal correlation of the estimations is exploited through the prediction-update process. The proposed approach is compared to existing methods using both simulations and experimental results. It was found that the proposed algorithm can eliminate the common biases and reduce the localization error to below 1 meter under open sky conditions.