Road has a direct impact on vehicle ride comfort and handling stability

so that the real-time acquisition of road information plays an important role in improving the vehicle performance. The multiple types of road are difficultly identified accurately using traditional road identification methods. The convolutional neural network is used to identify the road type

and then the identified road type is used as the basis for tuning the controller parameters of suspension system in order to make the controllable suspension system maintain the optimal performance under different road surfaces. Firstly

the quarter-vehicle semi-active suspension model is established. Secondly

the basic structure of convolutional neural network is built

and this network is trained and tested based on four typical urban and non-urban road images collected in advance. And then genetic algorithm is used to obtain the optimal control parameters of suspension system under excitations of four different roads

such as asphalt road

sandstone road

pebble road and cement road. Finally

the suspension control parameters are adaptively adjusted according to both the identified and optimized results of road surface. The simulated results show that the road identification method based on convolutional neural network can accurately identify a variety of roads; the semi-active suspension control system based on road identification and genetic algorithm can adaptively adjust the suspension parameters according to different road surfaces

thus improving the vehicle performance effectively.