基于三轴加速度计智能轮胎的车道积水量化研究

doi:10.3901/JME.260445

机械工程学报 ›› 2026, Vol. 62 ›› Issue (8): 210-220.doi: 10.3901/JME.260445

• 特邀专辑：汽车线控底盘 • 上一篇下一篇

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基于三轴加速度计智能轮胎的车道积水量化研究

李波^1,2, 过锦飞², 贝绍轶², 许男¹

1. 吉林大学汽车底盘集成与仿生全国重点实验室长春 130022;
2. 江苏理工学院汽车与交通工程学院常州 213001

收稿日期:2025-02-14 修回日期:2025-10-15 出版日期:2026-04-20 发布日期:2026-06-12
作者简介:李波,男,1986年出生,博士,副教授。主要研究方向为智能轮胎、智能汽车动力学分析与控制。E-mail:blfly1985@126.com;许男(通信作者),男,1988年出生,博士。主要研究方向为车辆动力学与控制、智能轮胎。E-mail:xunan@jlu.edu.cn
基金资助:
国家自然科学基金(52172367);汽车底盘集成与仿生全国重点实验室开放基金(20230206);江苏省高校自然科学基金(21KJA580001);常州国际科技合作基金(CZ20220031)资助项目。

Research on Lane Water Accumulation Using Intelligent Tire with Embedded Accelerometer

LI Bo^1,2, GUO Jinfei², BEI Shaoyi², XU Nan¹

1. National Key Laboratory of Automotive Chassis Integration and Bionics, Jilin University, Changchun 130022;
2. School of Automotive Traffic Engineering, Jiangsu University of Technology, Changzhou 213001

Received:2025-02-14 Revised:2025-10-15 Online:2026-04-20 Published:2026-06-12

摘要/Abstract

摘要： 车道积水的存在是导致车辆失稳的关键性因素之一，较厚的积水甚至会导致车辆发生“水滑”。因此，实时了解车道的积水厚度对驾驶员和自动驾驶车辆至关重要。基于智能轮胎概念，提出一种基于三轴加速度计的车道积水量化方法。通过ABAQUS建立轮胎-水膜-车道三者间的流固耦合模型，分析轮胎内衬中心处的三维加速度信号，选取径向方向加速度信号作为研究对象。采集到信号后利用截止频率为450 Hz的巴特沃斯低通滤波对信号进行处理，旨在去除高频噪声。进行全局坐标系到局部坐标系的信号转换，获得特征值数据矩阵后搭建了基于BP神经网络的车道积水厚度预测模型。实车试验结果表明，提出的基于三轴加速度计的车道积水量化方法具有较高的精度以及较好的开发应用前景。

关键词: 智能轮胎, 路面湿度量化, 三轴加速度计, 神经网络

Abstract: The presence of water in the driveway is one of the key factors contributing to vehicle destabilization, and thicker water can even lead to “hydroplaning”. Therefore, real-time understanding of the thickness of accumulated water in the lane is crucial for drivers and autonomous vehicles. Based on the concept of smart tires, this paper proposes a triaxial accelerometer-based method to quantify the water accumulation in the lane. The fluid-solid coupling model between tire-water film-lane is established through ABAQUS, and the three-dimensional acceleration signal at the center of the tire liner is analyzed, and the radial direction acceleration signal is selected as the research object. The signals were acquired and processed using Butterworth low-pass filtering with a cutoff frequency of 450 Hz, aiming at removing high-frequency noise. The signal conversion from the global coordinate system to the local coordinate system is carried out, and the prediction model of lane water thickness based on BP neural network is constructed after obtaining the eigenvalue data matrix. The results of real-vehicle tests show that the triaxial accelerometer-based lane water accumulation quantification method proposed in this paper has high accuracy and good prospects for development and application.

Key words: intelligent tires, quantification of road surface humidity, triaxial accelerometer, neural network

中图分类号:

U463

李波, 过锦飞, 贝绍轶, 许男. 基于三轴加速度计智能轮胎的车道积水量化研究[J]. 机械工程学报, 2026, 62(8): 210-220.

LI Bo, GUO Jinfei, BEI Shaoyi, XU Nan. Research on Lane Water Accumulation Using Intelligent Tire with Embedded Accelerometer[J]. Journal of Mechanical Engineering, 2026, 62(8): 210-220.

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基于三轴加速度计智能轮胎的车道积水量化研究

Research on Lane Water Accumulation Using Intelligent Tire with Embedded Accelerometer

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