The popularity of private cars has brought great convenience to citizens’ travel. However, the number of private cars in society is increasing yearly, and the traffic pressure on the road is also increasing. The number of traffic accidents is increasing yearly, and the vast majority are caused by small private cars. Therefore, it is necessary to improve the traffic safety awareness of drivers and help car manufacturers to design traffic risk prediction systems. The Backpropagation neural network (BPNN) algorithm is used as the technical basis, combined with the MATLAB operation program, to simulate the driving process of the car. Dynamic predictive models are built to predict and analyze vehicle safety risks. Multiple experiments found that: (1) in various simulations, the simulation driving process of MATLAB is more in line with the actual car driving process; (2) the error between BPNN and the actual driving prediction is within 0.4, which can meet the actual needs. Predictive models are optimized to deploy and predict in various traffic situations. The model can effectively prompt risk accidents, reduce the probability of traffic accidents, provide a certain degree of protection for the lives of drivers and passengers, and significantly improve the safety of traffic roads.


    Access

    Download


    Export, share and cite



    Title :

    Vehicle Driving Risk Prediction Model by Reverse Artificial Intelligence Neural Network


    Contributors:


    Publication date :

    2022




    Type of media :

    Article (Journal)


    Type of material :

    Electronic Resource


    Language :

    Unknown






    Automatic driving danger prediction system based on artificial intelligence

    SHEN YANJIN / HUANG NING / ZHANG KUN et al. | European Patent Office | 2022

    Free access

    Vehicle driving assistance system based on artificial intelligence

    PAN XIAOGUANG / PAN ZHE / LINGHU BIN et al. | European Patent Office | 2021

    Free access

    Automatic driving vehicle speed control system based on artificial intelligence

    SHEN YANJIN / ZOU RUIRUI / ZHANG DAN et al. | European Patent Office | 2022

    Free access