  Urban environments pose a significant challenge for autonomous vehicles (AVs)
as they must safely navigate while in close proximity to many pedestrians. It
is crucial for the AV to correctly understand and predict the future
trajectories of pedestrians to avoid collision and plan a safe path. Deep
neural networks (DNNs) have shown promising results in accurately predicting
pedestrian trajectories, relying on large amounts of annotated real-world data
to learn pedestrian behavior. However, collecting and annotating these large
real-world pedestrian datasets is costly in both time and labor. This paper
describes a novel method using a stochastic sampling-based simulation to train
DNNs for pedestrian trajectory prediction with social interaction. Our novel
simulation method can generate vast amounts of automatically-annotated,
realistic, and naturalistic synthetic pedestrian trajectories based on small
amounts of real annotation. We then use such synthetic trajectories to train an
off-the-shelf state-of-the-art deep learning approach Social GAN (Generative
Adversarial Network) to perform pedestrian trajectory prediction. Our proposed
architecture, trained only using synthetic trajectories, achieves better
prediction results compared to those trained on human-annotated real-world data
using the same network. Our work demonstrates the effectiveness and potential
of using simulation as a substitution for human annotation efforts to train
high-performing prediction algorithms such as the DNNs.
