Investigating the Critical Characteristics of Pedestrian-Vehicle Game Modes at Unsignalized Crosswalks: Based on MCMC and BP Network

Abstract

Abstract There is weak traffic control at unsignalized crosswalks, and the operation of pedestrians and motor vehicles is based on their recognition of the surrounding road conditions, environment, and degree of danger. This is fundamentally a game process of mutual compliance and obstacles. Currently, there is still insufficient understanding of the characteristics and mechanisms of this game behavior. In this paper, a large number of human-vehicle interaction examples in the non-signaled pedestrian crossing are collected by UAV to analyze the pedestrian-vehicle interaction mode, and a comprehensive index called Pedestrian-Vehicle Game Index (PVGI) that depicts the pedestrian-vehicle game process considering the change of motion state is proposed. Then, the Markov-chain Monte Carlo (MCMC)has been used to identify the critical conditions for game modes. Additionally, a BN model based on the Gaussian Mixture Model (GMM) and the Expectation-Maximum algorithm (EM) algorithm is applied to model and analyze multiple games between pedestrians and vehicles. The results show that pedestrian-vehicle interaction includes 11 typical game modes in 3 categories, and there are significant differences in each interaction mode. MCMC identified the PVGI domain of the pedestrian-vehicle as [-4.0s, 2.0s]. In this game interval, the game mode will be divided into "pedestrian yield - vehicle dominant" and " vehicle yield - pedestrian dominant ", with corresponding game intervals of [-4.0, 0] and [0, 2.0]. The Naive Bayes (NB) model for second-round game recognition based on the EM algorithm and GMM model performs better, with a total accuracy of 83.78%.