LG-Traj: LLM Guided Pedestrian Trajectory Prediction
Pranav Singh Chib Pravendra Singh
Accurate pedestrian trajectory prediction is crucial for various
applications, and it requires a deep understanding of pedestrian motion
patterns in dynamic environments. However, existing pedestrian trajectory
prediction methods still need more exploration to fully leverage these motion
patterns. This paper investigates the possibilities of using Large Language
Models (LLMs) to improve pedestrian trajectory prediction tasks by inducing
motion cues. We introduce LG-Traj, a novel approach incorporating LLMs to
generate motion cues present in pedestrian past/observed trajectories. Our
approach also incorporates motion cues present in pedestrian future
trajectories by clustering future trajectories of training data using a mixture
of Gaussians. These motion cues, along with pedestrian coordinates, facilitate
a better understanding of the underlying representation. Furthermore, we
utilize singular value decomposition to augment the observed trajectories,
incorporating them into the model learning process to further enhance
representation learning. Our method employs a transformer-based architecture
comprising a motion encoder to model motion patterns and a social decoder to
capture social interactions among pedestrians. We demonstrate the effectiveness
of our approach on popular pedestrian trajectory prediction benchmarks, namely
ETH-UCY and SDD, and present various ablation experiments to validate our
approach.