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    Trajectory Guidance for Connected Human-Driving Vehicles through the Interactions between Drivers and Roadside Units

    Source: Journal of Transportation Engineering, Part A: Systems:;2023:;Volume ( 149 ):;issue: 011::page 04023110-1
    Author:
    Pinlong Cai
    ,
    Guangquan Lu
    DOI: 10.1061/JTEPBS.TEENG-7875
    Publisher: ASCE
    Abstract: Utilizing massive real-time traffic information in the vehicle-to-everything (V2X) environment, road traffic systems can be enhanced by optimizing vehicle trajectory patterns. Because intelligent decisions can be made by roadside units (RSUs) with multiaccess edge computing (MEC) devices, this paper presents a trajectory guidance method for connected human-driving vehicles (CHVs) based on human–RSU interactions. Optimal guidance commands were determined based on a trajectory predictive control method, helping drivers operate the vehicles to follow the expected trajectories. We utilized the Gaussian mixture model to analyze the naturalistic driving data set collected by the project of the Next Generation Simulation (NGSIM) and determine the acceleration distributions of different guidance commands, including decelerate rapidly, decelerate slowly, keep velocity, accelerate slowly, and accelerate rapidly. The Monte Carlo sampling method was used to simulate different acceleration choices for command-based guidance information, considering human driver uncertainty. Sensitivity analysis was conducted to evaluate the performance of the proposed trajectory guidance method with different parameters. Experimental results showed that the average trajectory deviations at all positions are less than 5 m, indicating that guidance performance with reasonable guidance parameters is acceptable. Therefore, the proposed trajectory guidance method by human–RSU interaction can effectively support CHVs participating in V2X cooperation and has good practical application prospects.
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      Trajectory Guidance for Connected Human-Driving Vehicles through the Interactions between Drivers and Roadside Units

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    http://yetl.yabesh.ir/yetl1/handle/yetl/4296265
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    contributor authorPinlong Cai
    contributor authorGuangquan Lu
    date accessioned2024-04-27T20:55:48Z
    date available2024-04-27T20:55:48Z
    date issued2023/11/01
    identifier other10.1061-JTEPBS.TEENG-7875.pdf
    identifier urihttp://yetl.yabesh.ir/yetl1/handle/yetl/4296265
    description abstractUtilizing massive real-time traffic information in the vehicle-to-everything (V2X) environment, road traffic systems can be enhanced by optimizing vehicle trajectory patterns. Because intelligent decisions can be made by roadside units (RSUs) with multiaccess edge computing (MEC) devices, this paper presents a trajectory guidance method for connected human-driving vehicles (CHVs) based on human–RSU interactions. Optimal guidance commands were determined based on a trajectory predictive control method, helping drivers operate the vehicles to follow the expected trajectories. We utilized the Gaussian mixture model to analyze the naturalistic driving data set collected by the project of the Next Generation Simulation (NGSIM) and determine the acceleration distributions of different guidance commands, including decelerate rapidly, decelerate slowly, keep velocity, accelerate slowly, and accelerate rapidly. The Monte Carlo sampling method was used to simulate different acceleration choices for command-based guidance information, considering human driver uncertainty. Sensitivity analysis was conducted to evaluate the performance of the proposed trajectory guidance method with different parameters. Experimental results showed that the average trajectory deviations at all positions are less than 5 m, indicating that guidance performance with reasonable guidance parameters is acceptable. Therefore, the proposed trajectory guidance method by human–RSU interaction can effectively support CHVs participating in V2X cooperation and has good practical application prospects.
    publisherASCE
    titleTrajectory Guidance for Connected Human-Driving Vehicles through the Interactions between Drivers and Roadside Units
    typeJournal Article
    journal volume149
    journal issue11
    journal titleJournal of Transportation Engineering, Part A: Systems
    identifier doi10.1061/JTEPBS.TEENG-7875
    journal fristpage04023110-1
    journal lastpage04023110-10
    page10
    treeJournal of Transportation Engineering, Part A: Systems:;2023:;Volume ( 149 ):;issue: 011
    contenttypeFulltext
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