Deep learning based road recognition for intelligent suspension systems
Jinwei Sun 1
,   Jingyu Cong 2  
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School of Vehicle Engineering, Xi’an Aeronautical University, Xi’an, China
School of Information and Communication Engineering, Hainan University, Haikou, China
Jingyu Cong   

School of Information and Communication Engineering, Hainan University, 570228, Haikou, China
Submission date: 2021-04-28
Final revision date: 2021-06-17
Acceptance date: 2021-06-22
Online publication date: 2021-07-21
Publication date: 2021-07-25
Journal of Theoretical and Applied Mechanics 2021;59(3):493–508
This paper presents a deep learning-based road recognition strategy for advanced suspension systems. A four-quarter suspension model with a magnetorheological (MR) damper is developed, and four typical road images with corresponding roughness data are collected. A back-propagation neural network based autoencoder and Convolutional Neural Networks (CNN) are utilized to form the deep learning structure. By utilizing the multi-object genetic algorithm, the optimal parameters can be obtained, and the control current can be adaptively adjusted. Simulation results indicate that the designed structure can identify the road type accurately, and the recognition-based control strategy can improve the suspension performance effectively.
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