Pedestrian Crossing Action Prediction: A Comparative Study Using a Novel Dataset

Abstract: Pedestrian crossing action prediction has been the subject of active research in recent years. Despite the inherent difficulty in obtaining representative real-world data, the research community has developed numerous cutting-edge algorithmic solutions. The existing datasets are constrained in their ability to capture a diverse range of pedestrian behaviors and environments, which limits their applicability in a broader context. In an effort to help align the research community’s contributions with real-world self-driving problems, we present a comparative study using the novel, diverse, and Europe-centric EuroCity Persons-Intention (ECP-I) dataset. The primary objective of this study is to assess the usability of the ECP-I dataset by identifying any shortcomings and limitations through a comprehensive analysis. In order to achieve this objective, we analyze the generalizability and cross-evaluate SOTA across multiple datasets in addition to our ECP-I dataset. As a result of this study, we present the performance changes through key differences between datasets. Our investigation of the ECP-I dataset represents a pioneering effort in the field of pedestrian crossing action prediction.