基于层次分析法(AHP)、K-means 聚类和 BP 神经网络算法提出了一种驾驶行为安全评估方方法.该方法在对采集的数据进行预处理之后,通过提取驾驶行为特征和利用AHP对其进行加权处理等对驾驶行为进行评分.在此基础上,利用 K-means 聚类算法,将驾驶行为划分为安全节能型、安全耗能型和激进耗能型等 3 类.研究利用BP神经网络算法对聚类结果进行学习,实现对驾驶行为的分类和安全性评估.实验结果表明:该方法可有效识别和评估驾驶员的驾驶行为,进而为驾驶员行为的安全管理提供参考.
Driving behavior safety assessment method based on deep learning fusion model
Based on the Analytic Hierarchy Process(AHP),K-means clustering,and Back Propagation(BP)neural network algorithms,a driving behavior safety assessment method is proposed to comprehensively evaluate and accurately classify driving behaviors.The method first preprocesses the collected data and scores driving behaviors by extracting driving behavior features and using AHP for weighted processing.Then,it employs the K-means clustering algorithm to categorize driving behaviors into three types:safe and energy-efficient,safe and energy-consuming,and aggressive and energy-consuming.Finally,it utilizes the BP neural network algorithm to learn from the clustering results,achieving classification and safety assessment of driving behaviors.Experimental results show that this method can effectively identify and assess the driving behaviors of drivers,thus providing a reference for the safety management of driver behaviors.
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