Abstract This paper proposes a robust road profile estimation method and vehicle parameter identification method through an optimization with an objective function and constraint conditions on estimated profiles. The methods require only vehicle response measurements, enabling easy and inexpensive, yet effective, road condition monitoring through the automated processing of smartphone data. A half-car (HC) model, representing both bouncing and pitching motions, is employed for the profile estimation. The road profiles at the front and rear tire locations are included as state variables in the augmented state vector and are estimated by combining the augmented Kalman filter (AKF), Robbins–Monro (RM) algorithm, and Rauch–Tung–Striebel (RTS) smoothing. The two independent state variables, however, correspond to a single physical profile, while their distance coordinates differ by the wheelbase. Therefore, the vehicle parameters are optimized through the minimization of the difference between the identified road profiles at the front and rear tire locations using a genetic algorithm. Three objective functions and three constraint conditions are proposed to automatically select the best vehicle parameters. With this HC model, the road profile is subsequently estimated by combining the AKF, RM, and RTS methods. Through numerical simulations, the accuracy of the profile estimation and validity of the parameter identification are clarified. The influences of different drive speeds and difference between the left and the right profiles are numerically investigated. Drive tests with three different vehicles and a reference laser profiler show that the algorithm can automatically compensate for differences among vehicles with different drive speeds and estimate profiles accurately.

中文翻译：

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通过智能手机数据的自动处理进行道路轮廓估计和半车模型识别

摘要 本文通过对估计轮廓的目标函数和约束条件进行优化，提出了一种鲁棒的道路轮廓估计方法和车辆参数识别方法。这些方法只需要车辆响应测量，通过智能手机数据的自动处理实现简单、廉价但有效的道路状况监测。代表弹跳和俯仰运动的半车 (HC) 模型用于轮廓估计。前后轮胎位置的道路轮廓作为状态变量包含在增强状态向量中，并通过组合增强卡尔曼滤波器 (AKF)、Robbins-Monro (RM) 算法和 Rauch-Tung-Striebel (RTS) 进行估计平滑。然而，两个独立的状态变量对应于单个物理配置文件，而它们的距离坐标因轴距而异。因此，通过使用遗传算法最小化前后轮胎位置识别出的道路轮廓之间的差异来优化车辆参数。提出了三个目标函数和三个约束条件来自动选择最佳车辆参数。使用此 HC 模型，随后通过组合 AKF、RM 和 RTS 方法来估计道路轮廓。通过数值模拟，阐明了剖面估计的准确性和参数辨识的有效性。数值研究了不同驱动速度和左右轮廓之间差异的影响。