Snapshot: Towards Application-centered Models for Pedestrian Trajectory Prediction in Urban Traffic Environments
<|reference_start|>Snapshot: Towards Application-centered Models for Pedestrian Trajectory Prediction in Urban Traffic Environments: This paper explores pedestrian trajectory prediction in urban traffic while focusing on both model accuracy and real-world applicability. While promising approaches exist, they are often not publicly available, 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 urban settings. Following this, we present Snapshot, a modular, feed-forward neural network that outperforms the current state of the art 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<|reference_end|>
@article{uhlemann2024snapshot:,
    title={Snapshot: Towards Application-centered Models for Pedestrian Trajectory
  Prediction in Urban Traffic Environments},
    author={Nico Uhlemann, Yipeng Zhou, Tobias Mohr, Markus Lienkamp},
    journal={arXiv preprint arXiv:2409.01971},
    year={2024},
    archivePrefix={arXiv},
    eprint={2409.01971},
    primaryClass={cs.CV}
}