  In this paper, we study the interaction between pedestrians and vehicles and
propose a novel neural network structure called the Pedestrian-Vehicle
Interaction (PVI) extractor for learning the pedestrian-vehicle interaction. We
implement the proposed PVI extractor on both sequential approaches (long
short-term memory (LSTM) models) and non-sequential approaches (convolutional
models). We use the Waymo Open Dataset that contains real-world urban traffic
scenes with both pedestrian and vehicle annotations. For the LSTM-based models,
our proposed model is compared with Social-LSTM and Social-GAN, and using our
proposed PVI extractor reduces the average displacement error (ADE) and the
final displacement error (FDE) by 7.46% and 5.24%, respectively. For the
convolutional-based models, our proposed model is compared with Social-STGCNN
and Social-IWSTCNN, and using our proposed PVI extractor reduces the ADE and
FDE by 2.10% and 1.27%, respectively. The results show that the
pedestrian-vehicle interaction influences pedestrian behavior, and the models
using the proposed PVI extractor can capture the interaction between
pedestrians and vehicles, and thereby outperform the compared methods.
