In the future, the need for traffic lights at intersections will be eliminated, and autonomous vehicles can be efficiently and safely controlled through an Intersection Control Center (ICC). This paper presents a novel unsignalized intersection control algorithm based on mixed integer nonlinear programming (MINP). By formulating the vehicle space-time trajectory conflict as a mathematical model, the algorithm takes into account various factors, including vehicle delay, fuel consumption, and dynamics constraints. The MINP problem is solved using a genetic algorithm to obtain the optimal vehicle trajectory. Moreover, recognizing the significance of network communication in intersection scheduling, the control algorithm also considers the influence of C-V2X communication performance. To validate the accuracy and effectiveness of the proposed algorithm, a comprehensive joint simulation is conducted using SUMO and MATLAB, which replicates real urban intersections. Experimental results demonstrate that the proposed algorithm outperforms traditional signal light control in terms of vehicle delay and fuel consumption.


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    Title :

    Optimizing Cooperative Control Algorithms for Vehicles and Roads at Unsignalized Intersections Considering Communication Performance


    Contributors:
    Wu, Xian (author) / Wei, Yiying (author) / Zou, Lin (author)


    Publication date :

    2023-12-01


    Size :

    544755 byte




    Type of media :

    Conference paper


    Type of material :

    Electronic Resource


    Language :

    English



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