Research On The Application Of Vehicle Safety Quantitative Management Under The Data Of Internet Of Vehicles

In recent years,although the number of casualties in traffic accidents in China has decreased,the number of casualties is still huge,and the research on urban road traffic safety still has a long way to go.Therefore,from this perspective,this article studies vehicle safety management,and proposes corresponding control methods and suggestions,which will help improve the level of vehicle safety management,fundamentally avoid accidents,and have a positive effect on identifying road traffic risk points and preventing traffic accidents.Based on the alarm data of the Internet of Vehicles,this paper constructs a vehicle safety quantitative management system that includes vehicle risk assessment,vehicle classification early warning,and vehicle safety quantitative management model.It realizes a comprehensive assessment of vehicle driving risk and proposes a classification warning method for dangerous driving behaviors.To study the significance of the impact of different dangerous driving factors on the vehicle risk level and quantify the degree of impact,step by step,apply the driving behavior research conclusions to the quantitative management of vehicle safety,forming a complete quantitative management system for vehicle safety,and make the vehicle safety management more effective.In terms of refinement,it takes a step forward.At the same time,the Internet of Vehicles technology and data are applied to the field of identifying and controlling risks to provide auxiliary decision-making for vehicle safety management.The specific research contents are as follows:(1)The processing and analysis of Internet of Vehicles data in the application scenario of vehicle safety management.The advanced driving assistance system,the collection of car networking alarm data and the characteristics of the alarm data in the environment of the car networking environment are analyzed,and the advantages of car networking alarm data for vehicle safety risk assessment are clarified.(2)Construction of a vehicle safety risk evaluation index system.According to the characteristics of the alarm data indicators of the Internet of Vehicles and the nature of the problem to be studied,combined with the characteristics of the vehicle alarm data,the dangerous behavior indicators are divided into two categories:assessment indicators and reduction indicators.The assessment indicators refer to conventional driving indicators,and the reduction indicators include Distracted driving,fatigue driving,speeding,etc.(3)Construct vehicle driving risk assessment model and conduct empirical research.To evaluate the traffic risks of vehicles during natural driving,this paper adopts the combination weighting method and fuzzy comprehensive evaluation to quantify the vehicle driving risks.The risk quantitative classification method is given,and the result of vehicle driving risk level is determined;finally,the use of association rules fully verifies the validity of the vehicle driving risk assessment model.(4)Construct a real-time classified early warning model of risk of abnormal driving behavior and conduct empirical research.Based on the vehicle driving risk assessment model,analyze the evaluation indicators that can achieve classified early warning,and retain the vehicle driving risk assessment index system,which can be artificially intervened to achieve vehicle safety and efficient management.A classified early warning model for the dangers of unsafe driving behaviors on expressways has been established to realize short-term early warning of hidden dangers.The comprehensive risk rating results of vehicles are applied to the quantitative management of vehicle safety,and the applicability of the model is verified with actual sample data.(5)Construct a quantitative management model for vehicle driving safety and conduct empirical research.Abnormal driving behavior is the direct cause of road traffic safety risks.It is necessary to study the influencing factors of vehicle driving risk for abnormal driving behavior.This paper uses a multivariate and ordered logistic model to calculate the amount of information and importance of different factors affecting abnormal driving behavior.Objectively calculate the significance of the impact of various abnormal driving behavior factors on the risk level of the vehicle,and use sample data to conduct empirical research.The empirical results show:TimeLength_fatigue,Mileage_fatigue,g_fatigue And Line has a strong ability to interpret the risk level of vehicles,which will significantly affect the number of vehicle accidents,which requires key control.Therefore,the traffic management department should focus on strengthening the speed limit and set corresponding regulations on lane departure behavior.At the same time,the traffic management department can perform combined control on driving behavior indicators that have a significant impact based on the results of the model risk quantification to achieve vehicle safety Optimal management and control to achieve a balance between traffic efficiency and traffic safety.