论文标题
使用机器学习中智能轮胎中的轮胎力估算
Tire Force Estimation in Intelligent Tires Using Machine Learning
论文作者
论文摘要
智能轮胎的概念吸引了研究人员在自动驾驶，高级车辆控制和人工智能领域的关注。本文的重点是智能轮胎以及机器学习技术在轮胎估计中的应用。我们提出了一个具有三轴加速传感器的智能轮胎系统，该系统安装在轮胎的内衬上，以及用于实时处理传感器数据的神经网络技术。加速度计能够在X，Y和Z方向上测量加速度。当加速度计进入轮胎接触贴片时，它开始生成信号，直到完全离开它。同时，通过使用MTS Flat-Trac测试平台，测量了轮胎实际力。加速度计和MTS Flat-Trac测试系统生成的信号用于训练三种不同的机器学习技术，目的是在线预测轮胎力。结果表明，发达的智能轮胎与机器学习结合使用，可以有效地预测不同驾驶条件下的轮胎力。这项工作中介绍的结果将在智能轮胎，车辆系统和轮胎力估算的领域开设新的研究途径。
The concept of intelligent tires has drawn attention of researchers in the areas of autonomous driving, advanced vehicle control, and artificial intelligence. The focus of this paper is on intelligent tires and the application of machine learning techniques to tire force estimation. We present an intelligent tire system with a tri-axial acceleration sensor, which is installed onto the inner liner of the tire, and Neural Network techniques for real-time processing of the sensor data. The accelerometer is capable of measuring the acceleration in x,y, and z directions. When the accelerometer enters the tire contact patch, it starts generating signals until it fully leaves it. Simultaneously, by using MTS Flat-Trac test platform, tire actual forces are measured. Signals generated by the accelerometer and MTS Flat-Trac testing system are used for training three different machine learning techniques with the purpose of online prediction of tire forces. It is shown that the developed intelligent tire in conjunction with machine learning is effective in accurate prediction of tire forces under different driving conditions. The results presented in this work will open a new avenue of research in the area of intelligent tires, vehicle systems, and tire force estimation.
