Snapshot: Towards Application-centered Models for Pedestrian Trajectory Prediction in Urban Traffic Environments

Nico Uhlemann, Yipeng Zhou, Tobias Simeon Mohr, Markus Lienkamp; Proceedings of the Winter Conference on Applications of Computer Vision (WACV) Workshops, 2025, pp. 1152-1162

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.