论文标题
纵向车辆动力学的Fisher可识别性分析
Fisher Identifiability Analysis of Longitudinal Vehicle Dynamics
论文作者
论文摘要
本文研究了纵向车辆动力学参数的估计准确性的理论cramer-rao界限。该分析是由参数估计值在各种应用中的值（包括机箱模型验证和主动安全性）激发的。相关文献通过能够估算各种条件的底盘参数的算法来解决这一需求。尽管已经研究了这种算法的实施，但其准确性的基本限制的问题在很大程度上尚未得到探索。我们通过提出两个贡献来解决这个问题。首先，本文提出了理论发现，这些发现揭示了基于车辆机箱参数可识别性的主要影响。然后，我们使用来自公路实验的数据来验证这些发现。我们的结果表明，在各种效果中，道路等级变异性在确定驱动周期的参数可识别性方面的强大相关性。这些发现可以激发未来改进的实验设计。
This paper investigates the theoretical Cramer-Rao bounds on estimation accuracy of longitudinal vehicle dynamics parameters. This analysis is motivated by the value of parameter estimation in various applications, including chassis model validation and active safety. Relevant literature addresses this demand through algorithms capable of estimating chassis parameters for diverse conditions. While the implementation of such algorithms has been studied, the question of fundamental limits on their accuracy remains largely unexplored. We address this question by presenting two contributions. First, this paper presents theoretical findings which reveal the prevailing effects underpinning vehicle chassis parameter identifiability. We then validate these findings with data from on-road experiments. Our results demonstrate, among a variety of effects, the strong relevance of road grade variability in determining parameter identifiability from a drive cycle. These findings can motivate improved experimental designs in the future.
