Construction of Driving Condition Based on Discrete Fourier Transform and Improved K-Means Clustering Algorithm

Abstract

Abstract In view of the low execution efficiency and slow convergence speed of traditional clustering algorithms, the initial clustering center has a greater impact on the clustering results, which leads to the problem of reduced algorithm accuracy. This paper proposes an improved K-means algorithm (Grid-K-means), that is, the Grid density is used to determine the initial clustering center; According to the density, the grid points are sorted to eliminate the idea of noise grid points and invalid grid points, so as to improve the efficiency and accuracy of the algorithm. First, the discrete Fourier transform was used to filter the original data, and then the principal component analysis and the improved K-means clustering algorithm were used to reduce and classify the kinematics fragments respectively, so as to construct the driving conditions of the vehicle. The experimental results show that this method can effectively improve the construction accuracy and reduce the construction time, and the fitted driving conditions can effectively reflect the local actual traffic conditions.