  Accurately predicting future pedestrian trajectories is crucial across
various domains. Due to the uncertainty in future pedestrian trajectories, it
is important to learn complex spatio-temporal representations in multi-agent
scenarios. To address this, we propose a novel Cross-Correction Framework (CCF)
to learn spatio-temporal representations of pedestrian trajectories better. Our
framework consists of two trajectory prediction models, known as subnets, which
share the same architecture and are trained with both cross-correction loss and
trajectory prediction loss. Cross-correction leverages the learning from both
subnets and enables them to refine their underlying representations of
trajectories through a mutual correction mechanism. Specifically, we use the
cross-correction loss to learn how to correct each other through an
inter-subnet interaction. To induce diverse learning among the subnets, we use
the transformed observed trajectories produced by a neural network as input to
one subnet and the original observed trajectories as input to the other subnet.
We utilize transformer-based encoder-decoder architecture for each subnet to
capture motion and social interaction among pedestrians. The encoder of the
transformer captures motion patterns in trajectories, while the decoder focuses
on pedestrian interactions with neighbors. Each subnet performs the primary
task of predicting future trajectories (a regression task) along with the
secondary task of classifying the predicted trajectories (a classification
task). Extensive experiments on real-world benchmark datasets such as ETH-UCY
and SDD demonstrate the efficacy of our proposed framework, CCF, in precisely
predicting pedestrian future trajectories. We also conducted several ablation
experiments to demonstrate the effectiveness of various modules and loss
functions used in our approach.
