Online Parameter Estimation Methods for Adaptive Cruise Control Systems
Online Parameter Estimation Methods for Adaptive Cruise Control Systems
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DOI：
10.1109/tiv.2020.3023674
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发表时间：
2019-11
影响因子：
8.2
通讯作者：
Yanbing Wang;George Gunter;Matthew Nice;M. D. Monache;D. Work
中科院分区：
文献类型：
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作者：
Yanbing Wang;George Gunter;Matthew Nice;M. D. Monache;D. Work
通过对自适应巡航控制(ACC)车辆进行建模，可以了解这些车辆对交通流量的影响。在这项工作中，两种在线方法被用来提供支持ACC的车辆的实时系统识别。第一种方法是递推最小二乘(RLS)方法，而第二种方法是通过粒子滤波(PF)来解决非线性联合状态和参数估计问题。我们提供了两种方法的参数可辨识性分析，以解析地表明，模型参数在均衡驱动下是不可辨识的。将在线方法的精确度和计算时间与常用的基于离线模拟的优化(即批优化)方法进行了比较。这些方法是在合成数据以及直接从2019年ACC车型上收集的经验数据上进行测试的，这些数据使用来自STOCK ACC系统一部分的传感器的数据。在线方法是可伸缩的，并提供与批处理方法相当的精度。对于中等大小(例如，15分钟)的数据集，RLS实时运行，并且比批处理方法快两个数量级。粒子过滤器还可以实时运行，并且还适用于数据集可以任意增大的流应用程序。
Modeling Adaptive Cruise Control (ACC) vehicles enables the understanding of the impact of these vehicles on traffic flow. In this work, two online methods are used to provide real time system identification of ACC enabled vehicles. The first technique is a recursive least squares (RLS) approach, while the second method solves a nonlinear joint state and parameter estimation problem via particle filtering (PF). We provide a parameter identifiability analysis for both methods to analytically show that the model parameters are not identifiable using equilibrium driving. The accuracy and computational runtime of the online methods are compared to a commonly used offline simulation-based optimization (i.e., batch optimization) approach. The methods are tested on synthetic data as well as on empirical data collected directly from a 2019 model year ACC vehicle using data from sensors that are part of the stock ACC system. The online methods are scalable and provide comparable accuracy to the batch method. RLS runs in real time and is two orders of magnitude faster than the batch method for modest sized (e.g., 15 min) datasets. The particle filter also runs in real-time, and is also suitable in streaming applications in which the datasets can grow arbitrarily large.