论文标题
使用高斯过程回归用于智能轮胎系统的横向力预测
Lateral Force Prediction using Gaussian Process Regression for Intelligent Tire Systems
论文作者
论文摘要
了解轮胎的动态行为及其与道路的相互作用在设计综合车辆控制策略中起着重要作用。因此，通过轮胎嵌入式传感器访问有关轮胎路相互作用的可靠信息，对于开发增强的车辆控制系统非常要求。因此，本研究工作的主要目标是i。为了分析来自基于实验加速度计的智能轮胎的数据，在各种操作中获得了不同的垂直载荷，速度和高滑动角的数据；和II。要开发基于机器学习工具的横向力预测变量，更具体地说是高斯过程回归（GPR）技术。据说，即使在高滑动角的情况下，提议的智能轮胎系统也可以提供有关轮胎路相互作用的可靠信息。此外，基于GPR的横向力模型可以以可接受的准确性预测力，并提供不确定性的水平，对于设计车辆控制策略非常有用。
Understanding the dynamic behavior of tires and their interactions with road plays an important role in designing integrated vehicle control strategies. Accordingly, having access to reliable information about the tire-road interactions through tire embedded sensors is very demanding for developing enhanced vehicle control systems. Thus, the main objectives of the present research work are i. to analyze data from an experimental accelerometer-based intelligent tire acquired over a wide range of maneuvers, with different vertical loads, velocities, and high slip angles; and ii. to develop a lateral force predictor based on a machine learning tool, more specifically the Gaussian Process Regression (GPR) technique. It is delineated that the proposed intelligent tire system can provide reliable information about the tire-road interactions even in the case of high slip angles. Besides, the lateral forces model based on GPR can predict forces with acceptable accuracy and provide level of uncertainties that can be very useful for designing vehicle control strategies.
