Analysis of pedestrian crossing behavior based on Centralized Unscented Kalman Filter and pedestrian awareness based social force model

With the development of autonomous driving, ensuring pedestrian safety has become a hot research topic. The traditional pedestrian crossing behavior research mostly track and predict pedestrian crossing from the view of vehicles, but do not have in-depth research on the impact of other elements in the traffic scene. This paper proposes a method for analyzing pedestrian crossing behavior based on roadside equipment for specific road sections. First, a pedestrian awareness-based social force model (PASFM) is proposed by introducing the factors that influence pedestrian crossing decisions in traffic scenarios into the social force model (SFM), including zebra crossings, fellow walkers and vehicles. To explore the psychological state of vehicles and pedestrians when they interact, evolutionary game theory is used to simulate the psychological characteristics of pedestrians when they encounter vehicles, and then reflect in the changes of the desired speed. Then Centralized Unscented Kalman Filter (CUKF) is proposed to complete the whole process of tracking and predicting the motion state of pedestrian crossing behavior, which uses PASFM as its prediction process. Finally, simulations and experiments are designed to show the effectiveness of our methods. Results show that the proposed method performs better during pedestrian tracking and can predict their crossing behavior to a certain extent.