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.
Pedestrian Intention Prediction and Style Recognition in Bird’s-Eye View
Sae Intl. J Cav
2023-11-16
Aufsatz (Konferenz)
Englisch
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