In this article, pedestrian crossing intention and pedestrian crossing style are
studied by means of statistical theory and artificial neural network. Feature
parameters such as the average speed of pedestrians, pedestrian attention to
vehicles, and vehicle arrival speed are extracted before and during the time
pedestrians cross the street from a bird’s-eye view. Based on these parameters,
an artificial neural network is used to predict the pedestrian crossing
intention. K-means statistical method was used to cluster the pedestrian
crossing styles, and the results showed that clustering the crossing styles into
three categories, conservative, cautious, and adventurous, has a better
classification effect, and the crossing behaviors of different types of
pedestrians were analyzed. A random forest-based model is used to identify
pedestrian crossing styles, the prediction accuracy reaches 91.83% and the
recognition accuracy reaches 93.3%. The research content of this article can
provide the foundation for intelligent driving pedestrian prediction, pedestrian
crossing scene simulation, and traffic management.
