Pedestrian Crossing Intention Prediction in the Wild: A Survey

In real-world driving scenarios, understanding the intentions of pedestrians in real-time is critical for the built environment safety when operating intelligent vehicles on the roads. Pedestrians crossing the street is a common behavior that can easily lead to accidents. This paper presents a comprehensive review of the prediction of pedestrian crossing intentions, focusing on data, model structure, data representation, information extraction, prediction function, and associated models and challenges. The review highlights that data types, model generalization ability, and prediction uncertainty are key challenges on pedestrian crossing intention prediction. It identifies open challenges and opportunities for future research in pedestrian crossing intention prediction.