Exploring Shared Gaussian Occupancies for Tracking-Free, Scene-Centric Pedestrian Motion Prediction in Autonomous Driving

Nico Uhlemann, Melina Wördehoff, Markus Lienkamp

2025

Abstract

This work introduces a scalable framework for pedestrian motion prediction in urban traffic, tailored for real-world applications in autonomous driving. Existing methods typically predict either individual objects, creating challenges with higher agent counts, or rely on discretized occupancy maps, sacrificing precision. To overcome these limitations, we propose a scene-centric transformer architecture with a cluster-based training approach, capturing pedestrian dynamics through combined probability distributions. This strategy enhances prediction efficiency as groups of nearby agents are unified into a shared representation, thus reducing computational load while still maintaining a continuous output format. Additionally, we investigate a tracking-free design, exploring the feasibility of accurate predictions based solely on object lists without explicit object association. To assess predictive performance, we compare our approach to state-of-the-art trajectory prediction methods, analyzing several metrics while keeping practical applications in mind. Evaluations on a dedicated pedestrian benchmark derived from the Argoverse 2 dataset demonstrate the model’s strong predictive accuracy and highlight the potential for tracking-free future developments.

Paper Citation

in Harvard Style

Uhlemann N., Wördehoff M. and Lienkamp M. (2025). Exploring Shared Gaussian Occupancies for Tracking-Free, Scene-Centric Pedestrian Motion Prediction in Autonomous Driving. In Proceedings of the 11th International Conference on Vehicle Technology and Intelligent Transport Systems - Volume 1: VEHITS; ISBN 978-989-758-745-0, SciTePress, pages 100-112. DOI: 10.5220/0013288900003941

in Bibtex Style

@conference{vehits25,
author={Nico Uhlemann and Melina Wördehoff and Markus Lienkamp},
title={Exploring Shared Gaussian Occupancies for Tracking-Free, Scene-Centric Pedestrian Motion Prediction in Autonomous Driving},
booktitle={Proceedings of the 11th International Conference on Vehicle Technology and Intelligent Transport Systems - Volume 1: VEHITS},
year={2025},
pages={100-112},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0013288900003941},
isbn={978-989-758-745-0},
}

in EndNote Style

TY - CONF

JO - Proceedings of the 11th International Conference on Vehicle Technology and Intelligent Transport Systems - Volume 1: VEHITS
TI - Exploring Shared Gaussian Occupancies for Tracking-Free, Scene-Centric Pedestrian Motion Prediction in Autonomous Driving
SN - 978-989-758-745-0
AU - Uhlemann N.
AU - Wördehoff M.
AU - Lienkamp M.
PY - 2025
SP - 100
EP - 112
DO - 10.5220/0013288900003941
PB - SciTePress