Road Roughness Level Identification Based on BiGRU Network

Abstract

A new method of road roughness level identification based on the bidirectional gated recurrent unit (BiGRU) network is proposed in this paper, which is contribute to solve the problems of intelligent chassis technology such as suspension control. Firstly, the vehicle vibration response data is attained by the ride comfort simulation of two-degree-of-freedom vehicle vibration model. Then the mapping relationship between the road roughness level and the vehicle vibration responses is determined, and the road roughness level identification model is established. The Adam algorithm and mini-batch gradient descent are utilized to improve the accuracy and increase the speed of the model training process. Finally, in order to verify the feasibility and practicability of the model, the ride comfort experiments are carried out on asphalt and brick roads. The results show that the accuracy of the road roughness level identification model based on the BiGRU network reaches 95.83%, and the recognition result is reliable. Moreover, the experimental road level can be successfully identified through the road roughness level identification model, which has high engineering application value.

Keywords

• GRU
• road roughness level identification
• reverse analysis
• vehicle responses