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arXiv · 2409.01971

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

Nico Uhlemann·Yipeng Zhou·Tobias Simeon Mohr·Markus Lienkamp
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Abstract

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.

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Nico Uhlemann, Yipeng Zhou, Tobias Simeon Mohr, Markus Lienkamp. 2024-09-03. Snapshot: Towards Application-centered Models for Pedestrian Trajectory Prediction in Urban Traffic Environments. https://arxiv.org/abs/2409.01971

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