Stackelberg-Game-Based Intelligent Vehicle Decision Method for Merging Scenarios

doi:10.16183/j.cnki.jsjtu.2020.319

Abstract

Abstract:

Existing decision-making methods for intelligent vehicles do not consider factors such as the right of way information, polite driving of the vehicle, and limited perception range of the vehicle, which may easily lead to safety hazards in merging scenarios. Aimed at these problems, a Stackelberg-game-based decision-making method is proposed. This method constructs a game model combining the right of way and conducts parametric modeling of the merging scenarios. Then, the cooperation factor is introduced to design the corresponding profit function. Finally, the vehicle decision-making solution framework is designed to achieve the maximum value of decision-making benefits in this scenario. The experimental results illustrate that the proposed method can effectively improve the accuracy of vehicle decision-making behavior prediction on the datasets and the decision-making robustness in a high traffic density environment.

Key words: intelligent vehicle decision-making methods, Stackelberg-game, merging scenario, right of way, cooperation reward

CLC Number:

TP242.6

HU Yikai, ZHUANG Hanyang, WANG Chunxiang, YANG Ming. Stackelberg-Game-Based Intelligent Vehicle Decision Method for Merging Scenarios[J]. Journal of Shanghai Jiao Tong University, 2021, 55(8): 1027-1034.

Figures/Tables 7

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数据集	ζ/ %		MAE/(m·s^-1)
数据集	本文方法	文献[11]	本文方法	文献[11]
INTERACTION-1	85.5	82.7	0.27	0.61
INTERACTION-2	84.1	82.4	0.36	0.75
NGSIM-US 101	89.2	89.6	0.50	0.83