Abstract:

The identification of road roughness is one key technology of smart chassis such as semi?active suspension control. There is a lack of cheap， reliable， accurate and rapid method currently. This paper proposes a new real-time road roughness level identification method based on LSTM （Long Short?Term Memory） network and sequential wheel center acceleration. This method adopts sequential signals of wheel center acceleration instead of traditional statistical features. Based on the LSTM network’s strong feature capture capability for sequential signals， it can rapidly obtain road classification features without signal preprocessing， greatly reducing the calculation burden to realize real?time identification. For training set data， the acceleration signal can be obtained from experimental data， or calculated through vehicle transfer characteristics with white?noise?generated road with different power spectral density levels. This method requires only one time?domain acceleration signal without complex preprocessing. It can achieve rapid identification of road roughness grades at different vehicle speeds， damping coefficients， sprung mass and sampling time with high robustness.

Key words: road unevenness identification, LSTM, signal processing, deep learning, suspension control