With the advent of autonomous driving technologies, traffic control at intersections is expected to experience revolutionary changes. Various novel intersection control methods have been proposed in the existing literature, and they can be roughly divided into two categories: vehicle-based traffic control and movement-based traffic control. Movement-based traffic control can be treated as updated versions of the current intersection signal control with the incorporation of the performance of autonomous vehicle functions. Meanwhile, vehicle-based traffic control utilizes some brand-new methods, mostly in real-time fashion, to organize traffic at intersections for safe and efficient vehicle passages. However, to date, no systematic comparison between these two control categories has been performed to suggest their advantages and disadvantages. This paper conducts a series of numerical simulations under various traffic scenarios to perform a fair comparison of their performances. Specifically, we allow trajectory adjustments of incoming vehicles under movement-based traffic control, while for its vehicle-based counterpart, we implement two strategies, i.e., the first-come-first-serve strategy and the optimization-based strategy. Overall, the simulation results show that vehicle-based traffic control generally incurs a negligible delay when traffic demand is low but lead to an excessive queuing time as the traffic volume becomes high. We also discover that the comparison results can be influenced by other factors such as intersection layouts, traffic distribution and maturity of the autonomous driving technologies.


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    Title :

    Simulation Comparisons of Vehicle-Based and Movement-Based Traffic Control for Autonomous Vehicles at Isolated Intersections


    Contributors:
    Yang, Chen (author) / Lin, Xi (author) / Li, Meng (author) / He, Fang (author)

    Published in:

    Publication date :

    2022-12-01


    Size :

    4117253 byte




    Type of media :

    Article (Journal)


    Type of material :

    Electronic Resource


    Language :

    English



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