Traffic intersections are hubs in the transportation network, but traffic intersections have become areas with a high incidence of traffic accidents due to their many branch intersections, dense vehicles, and complex traffic conditions. However, the use of Internet of Vehicles related technologies to solve such problems has been It is considered an effective program. This article mainly introduces the traffic state recognition and traffic safety early warning methods in the environment of the Internet of Vehicles. This paper uses the Internet of Vehicles to detect the real-time dynamic speed of the vehicle, and establishes a potential time-discrete model. The model is solved by the Kalman filtering method, the road conditions are evaluated, and the model is revised using historical data to improve the accuracy of the traffic condition evaluation. The experimental results of this paper show that the safety early warning algorithm reduces the occurrence of traffic accidents by 13%, and reduces the rate of false alarms and false alarms. Finally, the impact of the full early warning system on reducing the severity of the accident is analyzed by comparing the speed of the vehicle when the accident occurs.


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    Titel :

    Traffic Safety Early Warning Methods in the Environment of Internet of Vehicles


    Weitere Titelangaben:

    Advs in Intelligent Syst., Computing


    Beteiligte:
    Xu, Zheng (Herausgeber:in) / Parizi, Reza M. (Herausgeber:in) / Loyola-González, Octavio (Herausgeber:in) / Zhang, Xiaolu (Herausgeber:in) / Wang, Rongxia (Autor:in)

    Kongress:

    The International Conference on Cyber Security Intelligence and Analytics ; 2021 ; Shenyang, China March 19, 2021 - March 20, 2021



    Erscheinungsdatum :

    2021-03-10


    Format / Umfang :

    8 pages





    Medientyp :

    Aufsatz/Kapitel (Buch)


    Format :

    Elektronische Ressource


    Sprache :

    Englisch




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