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

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 receiverspecific (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.

The Impact of Road Configuration in V2V-Based Cooperative Localization: Mathematical Analysis and Real-World Evaluation

“…If they are violated, however, the exact expression of the CMM error will still be valid but the asymptotic approximation will be inaccurate. The third assumption has been experimentally verified in [12] under open sky conditions. Consider a network of connected vehicles consisting of N vehicles.…”

“…These works present promising improvements on GNSS localization, while most of them utilize additional information from different sources. Improving the localization accuracy of these widespread GNSS without incurring additional hardware and infrastructure costs has motivated recent research activities on Cooperative Map Matching (CMM) [9], [12], [10]. CMM has been shown capable of improving Global Navigation Satellite System (GNSS) positioning of a group of connected vehicles through estimation and correction of the GNSS common localization error.…”

Estimation and navigation methods with limited information for autonomous urban driving

“…Furthermore, GPS measurements often experience multipath errors in urban environments, which cause positioning systems based on GPS to be an unreliable information source for autonomous agents . Even highly accurate, state‐of‐the‐art positioning systems struggle to provide the level of localization needed for autonomous driving due to the difficulties that dense urban environments present . Furthermore, the high‐fidelity maps that enable autonomous driving are highly sensitive to environmental changes and expensive to obtain in regard to time, money, and resources.…”

“…1 Even highly accurate, state-of-the-art positioning systems struggle to provide the level of localization needed for autonomous driving due to the difficulties that dense urban environments present. 2 Furthermore, the high-fidelity maps that enable autonomous driving are highly sensitive to environmental changes and expensive to obtain in regard to time, money, and resources. Storing these highly detailed maps on-board the vehicle is unfeasible for all maps in all locations or for environments that regularly change; for example, construction areas with resurfaced roads, repainted lane markings, and lane or road closings.…”

Autonomous Urban Localization and Navigation with Limited Information

“…For example, multipath errors and signal shadowing in dense urban environments make positioning systems based on GPS an unreliable information source for autonomous agents [1]. Even highly accurate, stateof-the-art positioning systems struggle to provide the level of localization needed for autonomous driving due to the difficulties that dense urban environments present [2]. Additionally, highly detailed maps typically needed for autonomous driving are highly sensitive to environmental changes, and are expensive to obtain in regard to time, money, and resources.…”