This paper explores pedestrian trajectory prediction in urban traffic while focusing on both model accuracy and real-world applicability. While promising approaches exist, they often revolve around pedestrian datasets excluding traffic-related information, or resemble architectures that are either not real-time capable or robust. To address these limitations, we first introduce a dedicated benchmark based on Argoverse 2, specifically targeting pedestrians in traffic environments. Following this, we present Snapshot, a modular, feed-forward neural network that outperforms the current state of the art, reducing the Average Displacement Error (ADE) by 8.8% while utilizing significantly less information. Despite its agent-centric encoding scheme, Snapshot demonstrates scalability, real-time performance, and robustness to varying motion histories. Moreover, by integrating Snapshot into a modular autonomous driving software stack, we showcase its real-world applicability.
@article{arxiv.2409.01971,
title = {Snapshot: Towards Application-centered Models for Pedestrian Trajectory Prediction in Urban Traffic Environments},
author = {Nico Uhlemann and Yipeng Zhou and Tobias Simeon Mohr and Markus Lienkamp},
journal= {arXiv preprint arXiv:2409.01971},
year = {2025}
}