Robust Inter-Vehicle Distance Measurement Using Cooperative Vehicle Localization

Abstract

Precise localization is critical to safety for connected and automated vehicles (CAV). The global navigation satellite system is the most common vehicle positioning method and has been widely studied to improve localization accuracy. In addition to single-vehicle localization, some recently developed CAV applications require accurate measurement of the inter-vehicle distance (IVD). Thus, this paper proposes a cooperative localization framework that shares the absolute position or pseudorange by using V2X communication devices to estimate the IVD. Four IVD estimation methods are presented: Absolute Position Differencing (APD), Pseudorange Differencing (PD), Single Differencing (SD) and Double Differencing (DD). Several static and dynamic experiments are conducted to evaluate and compare their measurement accuracy. The results show that the proposed methods may have different performances under different conditions. The DD shows the superior performance among the four methods if the uncorrelated errors are small or negligible (static experiment or dynamic experiment with open-sky conditions). When multi-path errors emerge due to the blocked GPS signal, the PD method using the original pseudorange is more effective because the uncorrelated errors cannot be eliminated by the differential technique.

Cooperative Positioning Algorithms for Estimating Inter-Vehicle Distance Using Multi-GNSS

“…However, high cost, poor performance in the adverse weather conditions, and limited perceptual fields remain major issues. The restricted perceptual range of these sensor-based approaches can be addressed via cooperative positioning algorithms [3]- [5]. In general, cooperative positioning algorithms are classified as either ranging-based or non ranging-based.…”

“…However, these approaches are often expensive since they require additional infrastructure and hardware to be implemented. The non ranging-based cooperative algorithms, on the other hand, provides the most cost-effective solutions by measuring the IVD directly from each vehicle's pseudorange information [3]- [5]. Since GNSS systems are undoubtedly low cost and can be employed independently in cooperative connected vehicles [3]- [5], several studies have been conducted as non-ranging cooperative positioning.…”

“…The non ranging-based cooperative algorithms, on the other hand, provides the most cost-effective solutions by measuring the IVD directly from each vehicle's pseudorange information [3]- [5]. Since GNSS systems are undoubtedly low cost and can be employed independently in cooperative connected vehicles [3]- [5], several studies have been conducted as non-ranging cooperative positioning. Müller et al [4] analyzed the use of GNSS double difference pseudoranges to estimate the relative position of two vehicles using two Bayesian filters.…”

“…Tahir et al [5] provided a theoretical framework for measuring IVD using GNSS measurements exchanged between vehicles. Recently, Wang et al [3] examined the performance of four non ranging-based cooperative algorithms for IVD estimation including Absolute Position Differencing (APD), Pseudorange Differencing (PD), Single Differencing (SD), and Double Differencing (DD) in static and dynamic experiments. Although the IVD estimation problem utilizing GNSS measurements have been extensively studied in the literature, they were limited to a single satellite system and did not take into account the Multi-Constellation Multi-Frequency system, despite the fact that these systems are becoming increasingly available to reach centimetre-level accuracy [11] and can increase the overall system robustness.…”

A Cooperative Positioning Method of Connected and Automated Vehicles with Direction-of-Arrival and Relative Distance Fusion

“…Connected and automated vehicles (CAV) are promising methods worldwide to improve traffic safety, enhance driving comfort, and reduce energy consumption [1][2][3]. As an essential CAV function, accurate localization technology is all-important for CAV to realize high-grade performances such as vehicle platooning [4] and cooperative merging on highway ramps [5,6] and has been widely studied.…”