Understanding Pedestrian Trajectory Prediction from the Planning Perspective

Abstract

Pedestrians are essential component in traffic, and with the advent of intelligent transportation systems, conflicts over right‐of‐way between pedestrians and other traffic participants have become prevalent. Predicting pedestrian intentions and motions is crucial for autonomous driving and intelligent transportation. However, pedestrian prediction faces challenges such as the variability of pedestrian intentions and the flexibility of their motions, as well as the need for highly generalization capability and lightweight modeling on autonomous vehicles with limited computing power. To face these challenge, this paper proposes a Social Artificial Potential Field (Social‐APF) method for pedestrian trajectory prediction. The method combines target intention feature and surrounding obstacles features. We test the performance of Social‐APF on several public datasets. The results demonstrate that the Social‐APF method outperforms state‐of‐the‐art approaches on the public datasets in both lightweight and model accuracy. © 2023 Institute of Electrical Engineer of Japan and Wiley Periodicals LLC.