基于逐步回归分析和BP_Adaboost算法的危险驾驶行为辨识

Identification of Dangerous Driving Behavior on Stepwise Regression Analysis and BP_adaboost Multi-classification Algorithm

为准确辨识车辆在行驶过程中可能出现的异常加减速,压线行驶,右侧超车驾驶行为,以便于及时给予驾驶员信息反馈和安全预警,使车辆保持安全的运行状态.首先通过虚拟驾驶仿真实验平台,采集驾驶行为的48种车辆运行数据对实验数据进行预处理,获得实验样本1492组;其次利用逐步回归分析对原始数据进行降维处理,并选取其中的最优回归模型获得特征参数;将提取的特征参数数据输入到BP_Adaboost多分类网络中,训练BP_Adaboost多分类网络,对上述驾驶行为进行识别;最后该模型与BP神经网络进行识别结果对比分析.结果表明模型识别率相较于BP神经网络提高了8.81%,达到92.93%,能进行更加有效的安全预警.

In order to recognize abnormal acceleration and deceleration,line pressing,overtaking on the right side and give the driver Information feedback and safety early warning in time and keep the vehicle in a safe running state.Firstly,48 kinds of drivers driving behavior data and 1492 sets of experimental samples were gathered by driving simulation experiments,the experimental data were pretreated.Secondly,using stepwise regression analysis to reduce the dimension of the original data and selecting the optimal regression model to obtain the characteristic parameters.The the characteristic parameters were input in a BP_Adaboost multi-classification identification model.BP-Adaboost multi-classification identification network was trained,and an identification model was built for driving behavior.Finally,The model is compared with stepwise regression analysis and BP neural network combination model.The results show that the recognition rate of this model is 8.81% higher than that of BP neural network,reaching 92.93% and the model is more effective in early safely warning.