  Pedestrian trajectory prediction in urban scenarios is essential for
automated driving. This task is challenging because the behavior of pedestrians
is influenced by both their own history paths and the interactions with others.
Previous research modeled these interactions with pooling mechanisms or
aggregating with hand-crafted attention weights. In this paper, we present the
Social Interaction-Weighted Spatio-Temporal Convolutional Neural Network
(Social-IWSTCNN), which includes both the spatial and the temporal features. We
propose a novel design, namely the Social Interaction Extractor, to learn the
spatial and social interaction features of pedestrians. Most previous works
used ETH and UCY datasets which include five scenes but do not cover urban
traffic scenarios extensively for training and evaluation. In this paper, we
use the recently released large-scale Waymo Open Dataset in urban traffic
scenarios, which includes 374 urban training scenes and 76 urban testing scenes
to analyze the performance of our proposed algorithm in comparison to the
state-of-the-art (SOTA) models. The results show that our algorithm outperforms
SOTA algorithms such as Social-LSTM, Social-GAN, and Social-STGCNN on both
Average Displacement Error (ADE) and Final Displacement Error (FDE).
Furthermore, our Social-IWSTCNN is 54.8 times faster in data pre-processing
speed, and 4.7 times faster in total test speed than the current best SOTA
algorithm Social-STGCNN.
