A Cascaded Learning Framework for Road Profile Estimation Using Multiple Heterogeneous Vehicles
A Cascaded Learning Framework for Road Profile Estimation Using Multiple Heterogeneous Vehicles
复制标题
使用多个异构车辆进行道路轮廓估计的级联学习框架
DOI：
10.1115/1.4055041
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发表时间：
2022
期刊：
影响因子：
--
通讯作者：
Zheng, Minghui
中科院分区：
文献类型：
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作者：
Chen, Zhu;Hajidavalloo, Mohammad R.;Li, Zhaojian;Zheng, Minghui
道路轮廓信息可用于增强车辆控制性能、乘客乘坐舒适度以及路线规划和优化。现有的道路轮廓估计算法主要是基于单车辆的，这通常是容易受到模型的不确定性和测量噪声。本技术简介提出了一种新的级联学习框架，该框架利用多个异构车辆来实现增强的估计。在这个框架中，每个单独的车辆首先通过一个标准的干扰观测器（DOB）进行本地估计，同时穿越所考虑的路段。然后设计学习滤波器来动态地连接车辆，并且利用来自一个车辆的初步估计来生成另一个车辆的学习信号。对于每个车辆，产生异构学习信号并将其添加到其估计循环中以用于估计增强，通过该估计增强，在多次迭代中改进了估计。进行了大量的数值研究，以验证所提出的方法的有效性与有前途的结果表明。
Road profile information can be utilized to enhance vehicle control performance, passenger ride comfort, and route planning and optimization. Existing road-profile estimation algorithms are mainly based on one single vehicle, which are usually susceptible to modeling uncertainties and measurement noises. This technical brief proposes a new cascaded learning framework that utilizes multiple heterogeneous vehicles to achieve enhanced estimation. In this framework, each individual vehicle first performs a local estimation via a standard disturbance observer (DOB) while traversing a considered road segment. Then learning filters are designed to dynamically connect the vehicles, and the preliminary estimates from one vehicle are utilized to generate the learning signal for another. For each vehicle, a heterogeneous learning signal is produced and added to its estimation loop for estimating enhancement, through which the estimations are improved over multiple iterations. Extensive numerical studies are carried out to validate the effectiveness of the proposed method with promising results demonstrated.