A Study To Electronic Data Identification Of Intelligent Vehicle Based On Decision Tree Algorithm

In today’s highly developed electronic information technology,all aspects of society are undergoing digital transformation.The digital transformation of the automotive industry is not only reflected in production processes,but also the emergence of intelligent vehicles.As the overall ownership of intelligent vehicles in society continues to rise,the incidence of traffic accidents involving intelligent vehicles is on the rise,and vehicle identification around intelligent vehicles has become a hot issue of concern.Traditional vehicle identification methods based on physical evidence traces and video images have inherent limitations,making them unsatisfactory in terms of identification scope and accuracy of identification opinions.The built-in vehicle driving event recorder in smart cars will record a large amount of data related to vehicle status,which is a supplement and extension to traditional vehicle identification.The volume of on-board electronic data is often large and complex.To find valuable evidence associated with traffic accident cases to be identified from the vast amount of data,it is necessary to rely on high-tech assistance.Data mining is an important branch of big data analysis technology.Through comparing the performance of various data mining algorithms and combining the topics discussed in this article,it is concluded that decision tree algorithm is a relatively suitable mining algorithm.The volume of vehicle data involved in an accident or case may reach the big data level.Decision tree algorithms have advantages in analyzing vehicle data due to their easy to understand and high interpretability.However,when processing large volume data,there may be low computational efficiency and reduced accuracy of the results.Therefore,it is necessary to optimize and expand the decision tree for big data,On this basis,it is applied to the identification and analysis of vehicle data.Through simulation experiments,this paper observes the feasibility and accuracy of decision tree algorithm in predicting and analyzing vehicle data,and proposes a vehicle identification method that integrates multidisciplinary methods.This method is suitable for complex identification scenarios such as single vehicle and multiple vehicle accidents in traffic accidents,and can provide reference and reference for the identification of other types of cases.This article will begin with an introduction to the definition of intelligent vehicles and the concept of intelligent vehicle data,analyze the role of intelligent vehicle data in accident traceability and vehicle damage assessment,analyze the advantages and disadvantages of traditional vehicle identification methods,and derive the advantages and potential of electronic data identification.Through decision tree based data analysis technology,this article proposes methods and ideas for intelligent vehicle identification,and analyzes future challenges based on simulation experiments,And the future development direction of technology.