Department of Smart Industrial Machine Technologies, Korea Institute of Machinery and Materials, 156 Gajeongbuk-Ro, Yuseong-Gu, Daejeon 34103, Korea.
Sensors (Basel). 2021 May 7;21(9):3233. doi: 10.3390/s21093233.
Vehicles today have many advanced driver assistance control systems that improve vehicle safety and comfort. With the development of more sophisticated vehicle electronic control and autonomous driving technology, the need and effort to estimate road surface conditions is increasing. In this paper, a real-time road surface classification algorithm, based on a deep neural network, is developed using a database collected through an intelligent tire sensor system with a three-axis accelerometer installed inside the tire. Two representative types of network, fully connected neural network (FCNN) and convolutional neural network (CNN), are learned with each of the three-axis acceleration sensor signals, and their performances were compared to obtain an optimal learning network result. The learning results show that the road surface type can be classified in real-time with sufficient accuracy when the longitudinal and vertical axis acceleration signals are trained with the CNN. In order to improve classification accuracy, a CNN with multiple input that can simultaneously learn 2-axis or 3-axis acceleration signals is suggested. In addition, by analyzing how the accuracy of the network is affected by number of classes and length of input data, which is related to delay of classification, the appropriate network can be selected according to the application. The proposed real-time road surface classification algorithm is expected to be utilized with various vehicle electronic control systems and makes a contribution to improving vehicle performance.
如今，车辆拥有许多先进的驾驶员辅助控制系统，可提高车辆的安全性和舒适性。随着更复杂的车辆电子控制和自动驾驶技术的发展，对估计路面状况的需求和努力也在增加。本文开发了一种基于深度神经网络的实时路面分类算法，该算法使用通过安装在轮胎内部的三轴加速度计的智能轮胎传感器系统收集的数据库进行开发。使用每个三轴加速度传感器信号学习了两种具有代表性的网络，即全连接神经网络（FCNN）和卷积神经网络（CNN），并对它们的性能进行了比较，以获得最佳的学习网络结果。学习结果表明，当使用 CNN 训练纵向和垂直轴加速度信号时，可以实时以足够的精度对路面类型进行分类。为了提高分类精度，建议使用具有多个输入的 CNN，该 CNN 可以同时学习 2 轴或 3 轴加速度信号。此外，通过分析网络的准确性如何受到与分类延迟有关的类别的数量和输入数据长度的影响，可以根据应用选择合适的网络。预计所提出的实时路面分类算法将与各种车辆电子控制系统一起使用，并为提高车辆性能做出贡献。
