基于K均值聚类分析的车辆横向稳定性判定方法

针对既有车辆失稳判定方法存在的不足,开展了车辆横向稳定性关于模式识别的研究,提出了一种基于K均值聚类分析的车辆横向稳定性判别方法.利用CarSim建立整车动力学模型,采用K均值聚类算法对车辆行驶状态数据进行离线聚类分析,得到离线聚类质心及其危险等级.搭建CarSim与Simulink联合仿真平台,计算车辆实时行驶数据点与离线聚类质心之间的欧氏距离,设计了车辆横向稳定性判定指标,对车辆行驶稳定性进行了在线识别.该判定方法充分利用车辆离线数据和实时数据,对车辆行驶状态数据进行数据挖掘.仿真结果表明,该判定方法能够准确实时量化车辆的行驶稳定性,为控制系统的介入时机与程度提供判据.

Judgment Method of Vehicle Lateral Stability Based on K Means Clustering Analysis

As for the shortcomings of the existing methods of vehicle instability determination, the study on pattern recognition of vehicle running stability was carried out, and a new method of judging the vehicle lateral stability based on K means clustering algorithm was proposed. The vehicle dynamics model was established by CarSim, and the offline clustering centers and its danger level were obtained by offline clustering analysis of vehicle running state data through K means clustering algorithm. Then, the CarSim and Simulink co-simulation platform was built and Euclidean distance between data points and cluster centroids was also calculated. Vehicle running stability criterion in Simulink was designed, and the vehicle running stability online was identified. This identification method made full use of the comparison of offline data and real time data for data mining of the vehicle running data. The simulation results show that the method can accurately and real-timely quantify the vehicle''s lateral stability considering various parameters, which can provide the criterion for intervention timing and degree of control system.