基于组合聚类的智能汽车横向稳定性判别方法*

doi:10.19562/j.chinasae.qcgc.2020.11.007

摘要/Abstract

摘要： 在研究传统车辆稳定性判别方法的基础上,基于神经网络和聚类分析的理论,提出了一种车辆横向稳定性判别方法。采用SOFM神经网络和K均值聚类相结合的组合聚类法,对采集的车辆行驶参数进行离线聚类分析,得到各聚类中心及其稳定性等级。应用均值法在线更新聚类中心,计算实时数据与聚类中心的距离,根据距离最小准则进行车辆稳定性实时判别。以轮胎力法为基准对该稳定性判别方法性能进行分析,最后将判别结果作为稳定性控制策略介入控制的依据,通过CarSim/Simulink联合仿真和硬件在环试验,验证了该稳定性判别方法的有效性和准确性。

关键词: 智能汽车, 稳定性判别, 数据挖掘, 自组织特征映射, K均值聚类

Abstract: On the basis of the research on the traditional judging methods of vehicle stability, a new method of judging vehicle lateral stability is proposed based on the theories of neural network and cluster analysis. A combined clustering method combining SOFM neural network and K-means clustering is adopted to conduct off-line cluster analysis on vehicle driving parameters collected and to obtain all clustering centers and their stability level. The mean value method is applied to online updating clustering centers, the distance between real-time data and clustering center is calculated, and the vehicle stability is judged real-time based on minimum distance criterion. The performance of that stability judging method is analyzed with the tire force method as reference. Finally, the judging results are taken as the basis for the involvement of the stability control strategy, and the effectiveness and accuracy of the method is verified through CarSim/Simulink joint simulation and hardware-in-the-loop test

Key words: intelligent vehicle, stability judgement, data mining, SOFM, K-means clustering

谷先广, 孟科委, 姚鑫鑫, 汪洪波. 基于组合聚类的智能汽车横向稳定性判别方法^*[J]. 汽车工程, 2020, 42(11): 1497-1505.

Gu Xianguang, Meng Kewei, Yao Xinxin, Wang Hongbo. Judging Method for Lateral Stability of Intelligent Vehicle Based on Combined Clustering[J]. Automotive Engineering, 2020, 42(11): 1497-1505.

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基于组合聚类的智能汽车横向稳定性判别方法^*

Judging Method for Lateral Stability of Intelligent Vehicle Based on Combined Clustering

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