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Tightly-Coupled Vehicle Positioning Method at Intersections Aided by UWB

机译：超宽带辅助交叉口的紧密耦合车辆定位方法

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摘要

Reliable and precise vehicle positioning is essential for most intelligent transportation applications as well as autonomous driving. Due to satellite signal blocking, it can be challenging to achieve continuous lane-level positioning in GPS-denied environments such as urban canyons and crossroads. In this paper, a positioning strategy utilizing ultra-wide band (UWB) and low-cost onboard sensors is proposed, aimed at tracking vehicles in typical urban scenarios (such as intersections). UWB tech offers the potential of achieving high ranging accuracy through its ability to resolve multipath and penetrate obstacles. However, not line of sight (NLOS) propagation still has a high occurrence in intricate urban intersections and may significantly deteriorate positioning accuracy. Hence, we present an autoregressive integrated moving average (ARIMA) model to first address the NLOS problem. Then, we propose a tightly-coupled multi sensor fusion algorithm, in which the fuzzy calibration logic (FCL) is designed and introduced to adaptively adjust the dependence on each received UWB measurement to effectively mitigate NLOS and multipath interferences. At last, the proposed strategy is evaluated through experiments. Ground test results validate that this low-cost approach has the potential to achieve accurate, reliable and continuous localization, regardless of the GPS working statue.
机译：对于大多数智能交通应用以及自动驾驶而言，可靠且精确的车辆定位至关重要。由于卫星信号的阻塞，在GPS限制的环境（例如城市峡谷和十字路口）中实现连续的车道水平定位可能是一个挑战。本文提出了一种利用超宽带（UWB）和低成本车载传感器的定位策略，旨在在典型的城市场景（例如十字路口）中跟踪车辆。 UWB技术具有解决多径和穿透障碍物的能力，具有实现高测距精度的潜力。但是，在复杂的城市交叉路口，视线（NLOS）传播仍然没有很高的发生率，并且可能会严重降低定位精度。因此，我们提出一种自回归综合移动平均（ARIMA）模型来首先解决NLOS问题。然后，我们提出了一种紧密耦合的多传感器融合算法，其中设计并引入了模糊校准逻辑（FCL），以自适应地调整对每个接收到的UWB测量值的依赖性，以有效减轻NLOS和多径干扰。最后，通过实验对提出的策略进行了评估。地面测试结果证明，不管GPS工作原理如何，这种低成本方法都具有实现准确，可靠和连续定位的潜力。

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