Autonomous intersection management has become a state-of-the-art control strategy customized for connected and autonomous vehicles. Combining the advantages of tile-based and conflict point-based approaches, this paper proposes a two-stage optimization method based on a developed intersection modeling approach. The first stage is a timing schedule optimization model, assigning vehicle arrival times at an intersection. Based on the output of the first stage, the second stage is a trajectory optimization model, which gives the eco-driving strategies. Moreover, a rolling optimization with a variable cycle length is adopted to run the method continuously. Simulation results show that the proposed method outperforms the genetic algorithm-based method in terms of computation time, and can reduce vehicle delay and fuel consumption by 89.48% and 46.84%, respectively, under different traffic demands compared to the first-come-first-serve method. Furthermore, the performance of the proposed method under asymmetric traffic demand is discussed. Sensitivity analyses suggest that (1) a long cycle length benefits the proposed method within certain limits and (2) a proper deceleration within the intersection can balance traffic delay with fuel consumption. In addition, an additional model with a heuristic rule is compared with the original timing schedule optimization model. It is found that reducing binaries in the first stage can make a tradeoff between the quality of the solution and efficiency, which can be used in conjunction with long cycles.


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    Title :

    A Two-Stage Optimization Method for Schedule and Trajectory of CAVs at an Isolated Autonomous Intersection


    Contributors:
    Yao, Zhihong (author) / Jiang, Haoran (author) / Jiang, Yangsheng (author) / Ran, Bin (author)


    Publication date :

    2023-03-01


    Size :

    6104870 byte




    Type of media :

    Article (Journal)


    Type of material :

    Electronic Resource


    Language :

    English





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