Publications by authors named "Yufeng Lian"
To enable the timely adjustment of the control strategy of automobile active safety systems, enhance their capacity to adapt to complex working conditions, and improve driving safety, this paper introduces a new method for predicting road surface state information and recognizing road adhesion coefficients using an enhanced version of the MobileNet V3 model. On one hand, the Squeeze-and-Excitation (SE) is replaced by the Convolutional Block Attention Module (CBAM). It can enhance the extraction of features effectively by considering both spatial and channel dimensions.
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- A new method is proposed to normalize road adhesion coefficients and tire cornering stiffness, which is important for designing vehicle yaw-moment control systems.
- This method uses a fractional-order multi-variable gray model (FOMVGM) to create training and testing data for a long short-term memory (LSTM) network, which predicts how tire stiffness changes with road conditions.
- Simulations show that this normalization method effectively aids in developing robust vehicle control systems and understanding vehicle dynamics during different driving conditions.