A Road Roughness Estimation Method based on PSO-LSTM Neural Network

The development of intelligent and networked vehicles has enhanced the demand for precise road information perception. Detailed and accurate road surface information is essential to intelligent driving decisions and annotation of road surface semantics in high-precision maps. As one of the key parameters of road information, road roughness significantly impacts vehicle driving safety and comfort for passengers. To reach a rapid and accurate estimation of road roughness, in this study, we develop a neural network model based on vehicle response data by optimizing a long-short term memory (LSTM) network through the particle swarm algorithm (PSO), which fits non-linear systems and predicts the output of time series data such as road roughness precisely. We establish a feature dataset based on the vehicle response time domain data that can be easily obtained, such as the vehicle wheel center acceleration and pitch rate. A PSO-LSTM network is built to achieve road roughness estimation and prediction, which is compared to the common LSTM network, the backpropagation (BP) neural network, and the wavelet neural network by conducting experiments to evaluate the performance and robustness under different vehicle simulation velocities. The results demonstrate the ability of the proposed model to achieve more precise road roughness estimation, superior prediction accuracy, and better velocity robustness.