In the advanced driver assistance system (ADAS) and autonomous driving systems, accurate information on preceding vehicle motion states is vital for the path planning and control. To develop those intelligent driving systems, a modular integrated estimation algorithm for the preceding vehicle longitudinal and lateral states is proposed in this paper, the coupled nonlinear characteristics of vehicle dynamics are applied to improve state estimation accuracy. First, considering driver aggressiveness, a linear moving horizon estimator for the vehicle longitudinal speed is designed based on the car following model. Then, the estimated vehicle longitudinal speed is delivered to the lateral estimator module in real-time. For the arbitrary driving routes, the Serret-Frenet equations and nonlinear lateral dynamics are combined to describe the lateral motion of preceding vehicle. Furthermore, a nonlinear moving horizon estimator is constructed to estimate vehicle lateral states accurately. To reduce the computational burden, the multiple shooting (MS) method is introduced, then the objective function is divided into several segments to improve the strong nonlinearity produced by multiple iterations. The proposed estimation algorithm can effectively handle the initial biases, the additive biases and model error, which are common in the acquired signals, and provide an accurate estimate of preceding vehicle states.

中文翻译：

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基于移动水平估计的前车实时纵向和横向状态估计


在高级驾驶员辅助系统（ADAS）和自动驾驶系统中，先前车辆运动状态的准确信息对于路径规划和控制至关重要。为了开发这些智能驾驶系统，本文提出了一种针对前车纵向和横向状态的模块化集成估计算法，利用车辆动力学耦合非线性特性来提高状态估计精度。首先，考虑驾驶员的攻击性，基于汽车跟随模型设计了车辆纵向速度的线性移动水平估计器。然后，估计的车辆纵向速度被实时传送到横向估计器模块。对于任意行驶路线，结合Serret-Frenet方程和非线性横向动力学来描述前车的横向运动。此外，构建了非线性移动水平估计器来准确估计车辆横向状态。为了减轻计算负担，引入多次射击（MS）方法，然后将目标函数分为若干段，以改善多次迭代产生的强非线性。所提出的估计算法可以有效地处理所获取信号中常见的初始偏差、加性偏差和模型误差，并提供对先前车辆状态的准确估计。