Multi-vehicle Cooperative Decision-making and Trajectory Planning Based on Stackelberg Game Theory in Mixed Driving Environments

被引：0
|
作者：
Yan Y.-J. [1 ]
Peng L. [1 ]
Wang J.-X. [1 ]
Pi D.-W. [2 ]
Liu Y.-H. [3 ]
Yin G.-D. [1 ]
机构：
[1] School of Mechanical Engineering, Southeast University, Jiangsu, Nanjing
[2] School of Mechanical Engineering, Nanjing University of Science and Technology, liangsu, Nanjing
[3] School of Vehicle and Mobility, Tsinghua University, Beijing
来源：
Zhongguo Gonglu Xuebao/China Journal of Highway and Transport | 2024年 / 37卷 / 03期
基金：
中国国家自然科学基金;
关键词：
automotive engineering; autonomous driving vehicle; decision-making and trajectory planning; model predictive control; multi-vehicle interaction framework in mixed driving environment; Stackelberg game theory;
D O I：
10.19721/j.cnki.1001-7372.2024.03.004
中图分类号：
学科分类号：
摘要：
In a complex traffic environment where autonomous and human-driven vehicles coexist, reducing the influence of complex interactions between two vehicle types, which have drastically different driving behaviors on vehicle driving safety, ride comfort, and traffic efficiency is a key issue that needs to be addressed in the field of autonomous driving decision-making and control. Accordingly, this study proposed a non-cooperative game interaction framework between human-driven vehicles (HV) and autonomous vehicles (AV) in a mixed driving environment. First, a longitudinal game strategy for human-driven vehicles was established, considering the driver's longitudinal control characteristics of linearly decreasing vehicle acceleration, differentiated coordination degree, and different characteristics of time delay. Second, a longitudinal game strategy for autonomous vehicles was designed, considering the safety constraints of autonomous vehicles and surrounding vehicles, as well as the comfort and traffic efficiency objectives constraining the autonomous vehicles during the lane-changing process. Then, the interactions between human-driven vehicles and autonomous vehicles in different mixed-driving environments were solved based on the Stackelberg game theory to obtain the optimal lane-changing gaps and longitudinal speed trajectories of autonomous vehicles. The model predictive control (MPC) method was used to generate safe lateral lane-changing trajectories for autonomous vehicles. Finally, multiple sets of mixed driving conditions were designed according to the differences in the coordination degree and response delay time of human-driven vehicles. The test results showed that autonomous vehicles could quickly and accurately identify the coordination degree of human-driven vehicles, select the optimal lane-changing gap, and cooperate with surrounding autonomous vehicles to merge into the target gap. During the lane-changing process, the autonomous vehicles always maintained a safe distance from the surrounding vehicles, and both the longitudinal and lateral accelerations of the lane-changing vehicle did not exceed 1.25 m · s-2at a speed of 20 m · s-1. Finally, the safety and comfort performance were guaranteed, verifying the effectiveness of the non-cooperative game interaction framework proposed in this study. © 2024 Chang'an University. All rights reserved.
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页码：117 / 133
页数：16
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