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Robust Inter-Vehicle Distance Measurement Using Cooperative Vehicle Localization

机译:使用合作车辆定位的强大的车辆间距离测量

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摘要

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.
机译:精确定位对于连接和自动车辆(CAV)的安全至关重要。全球导航卫星系统是最常见的车辆定位方法,并已广泛研究以提高本地化精度。除了单车本地化外,还有一些最近开发的CAV应用还需要精确测量车辆间距离(IVD)。因此,本文提出了一种协同本地化框架,其通过使用V2X通信设备来估计IVD来共享绝对位置或伪距。提出了四种IVD估计方法:绝对位置差异(APD),伪距离差异(PD),单个差异(SD)和双差异(DD)。进行了几个静态和动态实验以评估和比较它们的测量精度。结果表明,该方法在不同条件下可能具有不同的性能。 DD如果不相关的误差小或可忽略不计(静态实验或具有开阔天空条件的动态实验),则该DD显示了四种方法中的卓越性能。当由于堵塞的GPS信号而出现多路径错误时,使用原始伪距离的PD方法更有效,因为无法通过差分技术消除不相关的错误。

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