基于ReliefF-RBF的路面不平度识别算法研究OACSTPCD

Road surface unevenness significantly affects both the driving safety of road vehicles and their dynamic responses.However,the existing data-driven methods for road surface classification struggle to efficiently handle time-varying parameters and vehicle speeds.Meanwhile,the existing model-based road surface recognition algorithms require known and accurate vehicle models,facing the challenge of acquiring vehicle physical parameters in real-world applications.This paper proposes a novel pavement classification algorithm that begins by back-calculating the equivalent pavement profile,followed by data pre-processing.Subsequently,it computes time and frequency domain features for the equivalent pavement profile and response information,and key features are extracted using the ReliefF algorithm.A radial basis function neural network is used to construct a classifier for pavement grading and recognition.Finally,the robustness of the proposed algorithm is verified through simulation tests and real-vehicle tests with different vehicle parameters and speeds.