Research On The Integrates Model Of Accurate Classification And Real-time Warning Of Driving Risk

With the increase of the complexity of the traffic environment,the number of traffic accidents,the number of casualties and the resulting economic losses rise synchronously.At the same time,the development of vehicle networking technology makes the "human-vehicle-environment" interconnection into a transportation system,by using the massive data collected by vehicle networking technology,scholars can use data mining technology to systematically study the problem of driving risk management.However,when constructing driving risk prediction models,most studies did not consider the importance of the correlation,redundancy and characteristics of model indicators in predicting driving risks.In the study of driving risk,the historical data of drivers and the current real-time driving situation are not considered,and there is a lack of integrated driving risk assessment model suitable for vehicle network environment.For this reason,this thesis designs an index system of influencing factors of driving risk based on feature selection and puts forward an integrated model of accurate classification and real-time early warning of driving risk to manage the whole process of driving risk.The integrated model designed in this study scientifically manages the whole process of driving risk by grading in advance,realtime monitoring and early warning in the event,and updating historical data afterwards,while the classification of driving risk is to better manage the risk.Therefore,different early warning levels must be set for different risk situations,and this differentiated early warning strategy is conducive to targeted driving advice to drivers.Minimize driving risk.The purpose of this model is to make a fast and accurate classification of drivers with the combination of driver historical data and real-time driving status,and to give the risk warning corresponding to the level,so as to achieve the purpose of reducing driving risk.In addition,the model can also be used as a basis for car insurance companies to formulate car insurance plans and can also provide a basis for automobile manufacturers to provide personalized services to customers.The experimental results show that the driving risk influence factor index system based on feature selection can improve the operation efficiency of the integrated model.This thesis also tests the classification effect and risk early warning effect of the integrated model and verifies that the model can make accurate classification and real-time early warning for drivers according to the driver’s historical data and real-time status.It is verified that after receiving the risk early warning,the integrated model can accurately identify the changes of the real-time state of the driver,regardless of whether the driver has adjusted his behavior or not.And give a reasonable risk early warning level and driving suggestions.