  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.
