基于Canopy-K-means算法的半挂汽车列车行驶数据分析

为实时监测车辆行驶状态,建立了基于Canopy-K-means算法的车辆行驶安全特征分类模型.采用Canopy-K-means聚类算法对车辆行驶数据进行挖掘分析,以欧氏距离大小作为数据集属性间的相似性分类指标,得到表征不同行驶安全特征的离线聚类质心;搭建T ruckSim与Simulink联合仿真平台,设置定半径变车速和方向盘斜阶跃输入仿真工况对车辆行驶状态进行在线识别;同时为验证该方法在实车上的应用效果,设置相同工况对离线聚类质心进行验证分析.仿真和实车结果表明:基于Canopy-K-means算法的数据挖掘方法可以对不同行驶状态数据进行分类,得到的表征不同行驶安全特征的聚类质心能在一定程度上对车辆行驶稳定性进行评价,可以作为车辆控制和预警的判定依据.

Driving Data Analysis of Tractor Semi-trailer Based on Canopy-K-means Algorithm

In order to monitor the driving status of vehicles in real time, a classification model of vehicle driving safety features based on Canopy-K-means algorithm was established. Using Canopy-K-means clustering algorithm to mine and analyze vehicle driving data, using Euclidean distance as the similarity classification index between attributes of data set, to obtain offline clustering centroids representing different driving safety features; building TruckSim and Simulink joint simulation Platform, set the fixed radius variable speed and steering wheel oblique step input simulation working conditions to identify the vehicle''s driving status online; at the same time, in order to verify the application effect of the method on the real vehicle, the same working conditions were set to verify the offline clustering centroid analysis. The simulation and real vehicle results show that the data mining method based on the Canopy-K-means algorithm can classify different driving state data, and the obtained cluster centroids representing different driving safety characteristics can evaluate the driving stability of the vehicle to a certain extent, which can be used as the judgment basis for vehicle control and early warning.