基于智能轮胎系统的实时路面辨识技术

doi:10.19562/j.chinasae.qcgc.2024.04.007

Abstract

Abstract:

Under complex and extreme conditions， road adhesion coefficient is an important state parameter for tire force analysis and vehicle dynamics control. Compared with the method of model estimation， the intelligent tire technology can feed back the interaction information between the tire and the road surface to the vehicle control system. In this paper， a method of obtaining road adhesion coefficient of vehicle by combining intelligent tire system and machine learning is proposed. Firstly， considering the driving conditions， the sensor selection is carried out， and the intelligent tire hardware acquisition system based on MEMS three-axis acceleration sensor is developed， and the wireless transmission mode with simplified hardware structure is adopted. Secondly， the data set of machine learning training is collected by vehicle experiments by collecting real car test data on different road surfaces and the wheel-ground relationship and signal characteristics are analyzed. Finally， the feature learning of acceleration timing signal is realized by combining the advantages of CNN and LSTM. The effectiveness and accuracy of the proposed CNN-LSTM dual channel fusion neural network model are verified by comparing with the training results of other neural network models. The road identification scheme proposed in this paper realizes the goal of real-time road recognition， and the hardware and software architecture and neural network model are more suitable for vehicle system loading， providing real-time and accurate road information for vehicle motion control.

Key words: pavement recognition, smart tire, machine learning, signal analysis

Weidong Liu,Zongzhi Han,Zhenhai Gao,Yanhu Kang. Real-Time Pavement Recognition Technology Based on Intelligent Tire System[J].Automotive Engineering, 2024, 46(4): 617-625.

Figures/Tables 16

References 27

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参数	数值
尺寸	22 mm × 22 mm
质量	55 g
工作电压	DC 4.5~36 V
测量范围	±100g
灵敏度	20 mV/g
响应频率	0.01~5 000 Hz
工作温度	-40~120 ℃

方法	准确率 /%
方法	干沥青路面	湿沥青路面	冰雪路面
NN	82.01	81.21	79.98
CNN	90.70	84.28	81.39
LSTM	92.06	87.50	84.75
CNN-LSTM	93.28	90.04	86.31

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Real-Time Pavement Recognition Technology Based on Intelligent Tire System

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