关键词: 行人过街风格；人车微观交互模型；关注度算法；轨迹预测；行人过街预警

Abstract: Pedestrians are an indispensable part of the traffic system, and they are also the most flexible, complex, and changeable factors in the traffic scene. According to statistics, more than 90% of traffic accidents are caused by human factors. This paper mainly focuses on pedestrians and studies the early warning method for pedestrians crossing the street. It is established a microscopic interaction model of pedestrians and vehicles, and proposed an attention degree algorithm based on the deviation angle of the pedestrian’s head; collected a bird’s-eye view video of pedestrians and vehicles, calibrated the two-dimensional coordinate points of pedestrians and vehicles as trajectory data, and using trajectory data and heads, deviation angle calculates the maximum acceleration and the degree of attention, and clusters the two typical crossing styles with the maximum acceleration and the degree of attention. Analyze the warning degree of different street crossing styles. Finally, the generative confrontation network model is used to predict the trajectory of pedestrians during human-vehicle interaction; trace with a predicted trajectory describe the reaction of pedestrians to interactive vehicles, this experiment is useful for studying unsignalized intersections. The movement style and trajectory distribution of pedestrians crossing the street have important reference significance. Theoretically, the safety of pedestrian vehicle interaction at unsignaled intersections is enhanced.

Key words: Pedestrian crossing style; Human-vehicle micro-interaction model; Attention algorithm; Trajectory prediction; Warning for pedestrians crossing the street