Game Theory-based Simulation of Pedestrian Behavior at Right-turn Unsignalized Intersections

doi:10.3901/JME.2024.10.086

Abstract

Abstract: In order to realistically simulate the real traffic scene of pedestrian-vehicle interaction, this study integrates game theory and data-driven ideas, and proposes a game-deep maximum entropy inverse reinforcement learning algorithm(G-DMIRL), modeling pedestrians as intelligent bodies, obtaining the reward functions of pedestrians under different game decisions through real pedestrian-vehicle interaction trajectories, and inferring the game mechanism of pedestrian-vehicle interaction, and developing a simulation model of pedestrian behavior by using the obtained reward functions and action strategies. The simulation results show that the developed model can accurately simulate the behavioral actions of pedestrians under different decisions in a finite state, and the established pedestrian-vehicle traffic scenario can provide support for the development and validation of recognition, prediction and path planning algorithms for self-driving cars.

Key words: inverse reinforcement learning, reinforcement learning, human-vehicle interaction, game theory, right-turn intersection

CLC Number:

U492
TP391

LI Wenli, ZHANG Yinan, SHI Xiaohui, WANG Mengxin. Game Theory-based Simulation of Pedestrian Behavior at Right-turn Unsignalized Intersections[J]. Journal of Mechanical Engineering, 2024, 60(10): 86-101.

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