Trajectory Guidance for Connected Human-Driving Vehicles through the Interactions between Drivers and Roadside Units
Publication: Journal of Transportation Engineering, Part A: Systems
Volume 149, Issue 11
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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Data Availability Statement
Some or all data, models, or code that support the findings of this study are available from the corresponding author upon reasonable request.
Acknowledgments
This work was supported by the National Natural Science Foundation of China (No. 52131204) and the Science and Technology Commission of Shanghai Municipality (Nos. 22YF1461400 and 22DZ1100102).
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Journal of Transportation Engineering, Part A: Systems
Volume 149 • Issue 11 • November 2023
Copyright
© 2023 American Society of Civil Engineers.
History
Received: Dec 27, 2022
Accepted: Jul 14, 2023
Published online: Sep 12, 2023
Published in print: Nov 1, 2023
Discussion open until: Feb 12, 2024
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