Cloud-Assisted Collaborative Road Information Discovery With Gaussian Process: Application to Road Profile Estimation
Cloud-Assisted Collaborative Road Information Discovery With Gaussian Process: Application to Road Profile Estimation
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DOI：
10.1109/tits.2022.3194093
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发表时间：
2021-10
影响因子：
8.5
通讯作者：
Mohammad R. Hajidavalloo;Zhaojian Li;Xin Xia;Ali Louati;Minghui Zheng;Weichao Zhuang
中科院分区：
文献类型：
--
作者：
Mohammad R. Hajidavalloo;Zhaojian Li;Xin Xia;Ali Louati;Minghui Zheng;Weichao Zhuang
利用现代车辆作为移动传感器来获取重要的道路信息，如坑洼、黑冰和道路轮廓，这一点越来越受欢迎。这些信息的可用性已被确定为下一代车辆提高安全性、效率和舒适性的关键因素。然而，现有的道路信息发现方法主要是在单车辆环境下进行的，这不可避免地容易受到车辆模型不确定性和测量误差的影响。为了克服这些限制，本文提出了一种新的云辅助协同估计框架，该框架可以利用多个异构车辆迭代提高估计性能。具体来说，每辆车都将其车载测量数据与基于云的高斯过程（GP）相结合，将先前参与车辆的“伪测量”众包到一个本地估计器中以改进估计。由此产生的本地机载估计然后被发送回云端以更新GP，在那里我们使用噪声输入GP （NIGP）方法来明确处理不确定的GPS测量。我们将提出的框架应用于协同道路轮廓估计。广泛的仿真和硬件在环实验结果表明，尽管存在车辆异质性、模型不确定性和测量噪声，但所提出的协同估计可以显著增强估计能力，并迭代提高不同车辆的估计性能。
There is an increasing popularity in exploiting modern vehicles as mobile sensors to obtain important road information such as potholes, black ice and road profile. Availability of such information has been identified as a key enabler for next-generation vehicles with enhanced safety, efficiency, and comfort. However, existing road information discovery approaches have been predominately performed in a single-vehicle setting, which is inevitably susceptible to vehicle model uncertainty and measurement errors. To overcome these limitations, this paper presents a novel cloud-assisted collaborative estimation framework that can utilize multiple heterogeneous vehicles to iteratively enhance estimation performance. Specifically, each vehicle combines its onboard measurements with a cloud-based Gaussian process (GP), crowdsourced from prior participating vehicles as “pseudo-measurements”, into a local estimator to refine the estimation. The resultant local onboard estimation is then sent back to the cloud to update the GP, where we utilize a noisy input GP (NIGP) method to explicitly handle uncertain GPS measurements. We employ the proposed framework to the application of collaborative road profile estimation. Promising results on extensive simulations and hardware-in-the-loop experiments show that the proposed collaborative estimation can significantly enhance estimation and iteratively improve the performance from vehicle to vehicle, despite vehicle heterogeneity, model uncertainty, and measurement noises.