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.
Highlights
This paper develops four inter-vehicle distance estimation methods, i.e., Absolute Position Differencing (APD), Pseudorangs Differencing (PD), Single Differencing (SD) and Double Differencing (DD), based on cooperative vehicle localization
The results show that the DD method shows superior performance among the four methods if the uncorrelated errors are small or negligible
When the 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
Summary
Publisher’s Note: MDPI stays neutral with regard to jurisdictional claims in published maps and institutional affiliations. The non-ranging cooperative localization method is a cost-effective solution that directly uses the pseudorange information of each vehicle to estimate the IVD. In this paper, we explore several non-ranging cooperative localization methods to estimate the IVD for a group of connected vehicles, including Absolute Position Differencing (APD), Pseudorangs Differencing (PD), Single Differencing (SD) and Double Differencing (DD). Field experiments, including static and dynamic, open-sky and GNSS-blocked driving scenarios, were conducted to verify their effectiveness Among these methods, DD indicated the highest IVD. Sensors 2021, 21, 2048 including static and dynamic, open-sky and GNSS-blocked driving scenarios, were conducted to verify their effectiveness Among these methods, DD indicated the highest IVD measurement accuracy accuracy in the best accuracy in urmeasurement in open open sky skyconditions, conditions,while whilePD. Random variable and obeys the Gaussian distribution, whose mean is zero and variance is an σu i.e., nu (t) ∼ 0, σu
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