In order to support the research on the influence of connected autonomous vehicle on traffic flow, taking into account the influence sensitivity of multi front vehicles speed and acceleration and the influence weight within the communication range, connected longitudinal-control-model (C-LCM) model of connected autonomous vehicle is built based on the longitudinal-control-model (LCM). In the scenario of uniform headway and uniform traffic flow, the conditions for model stability were discussed by applying small disturbances. The relationship between the stability domain of the model and the number and position of the preceding vehicles was analyzed through simulation environment. After determining the values of C-LCM parameters, the impact of model parameters on model performance was explored in specific common scenarios such as acceleration, deceleration, and disturbance. The research results indicate that connected autonomous driving vehicles can obtain more information about the front vehicle, and give following vehicles more time to change their operating behavior to maintain stable traffic flow. At the same time, in disturbed scenarios, this car following model can reduce unexpected fluctuations in speed. The model has good stability and anti-interference ability.


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    Title :

    Modeling and stability analysis of car following model for connected autonomous driving


    Contributors:
    Shangguan, Wei (editor) / Wu, Jianqing (editor) / Yuan, Shoutong (author) / Li, Yan (author)

    Conference:

    Third International Conference on Intelligent Traffic Systems and Smart City (ITSSC 2023) ; 2023 ; Xi'an, China


    Published in:

    Proc. SPIE ; 12989 ; 129890Y


    Publication date :

    2024-04-09





    Type of media :

    Conference paper


    Type of material :

    Electronic Resource


    Language :

    English






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