Multi-Agent Trajectory Prediction for Urban Environments with UAV Data Using Enhanced Temporal Kolmogorov-Arnold Networks with Particle Swarm Optimization

Mohammad Reza Mohebbi, Mohammad Reza Mohebbi, Elahe Kafash, Mario Döller

2025

Abstract

Accurate trajectory prediction for moving agents such as pedestrians and vehicles is essential for autonomous driving, intelligent navigation, and abnormal behavior detection. Real-time prediction of future movements enhances the development of autonomous vehicles and the efficiency of traffic management systems. In this study, a novel trajectory prediction approach based on Temporal Kolmogorov-Arnold Networks (TKAN) is introduced, using the TUMDOT-MUC dataset collected by Unmanned Aerial Vehicles (UAVs) in Munich, Germany, to model large-scale urban scenarios. To improve prediction accuracy, additional features were extracted from the primary dataset and incorporated into the TKAN architecture, demonstrating a marked performance improvement over general machine learning models. The accuracy of predictions is further refined by tuning hyperparameters of TKAN through Particle Swarm Optimization (PSO). The proposed model provides a robust and reliable solution for the trajectory prediction of multi-agents in challenging urban traffic conditions. This research advances intelligent and effective transportation systems by proposing scalable methods for improved traffic management and safety in densely populated urban areas, ultimately contributing to smarter and more efficient transportation networks.

Paper Citation

in Harvard Style

Mohebbi M., Kafash E. and Döller M. (2025). Multi-Agent Trajectory Prediction for Urban Environments with UAV Data Using Enhanced Temporal Kolmogorov-Arnold Networks with Particle Swarm Optimization. In Proceedings of the 17th International Conference on Agents and Artificial Intelligence - Volume 2: ICAART; ISBN 978-989-758-737-5, SciTePress, pages 586-597. DOI: 10.5220/0013243100003890

in Bibtex Style

@conference{icaart25,
author={Mohammad Mohebbi and Elahe Kafash and Mario Döller},
title={Multi-Agent Trajectory Prediction for Urban Environments with UAV Data Using Enhanced Temporal Kolmogorov-Arnold Networks with Particle Swarm Optimization},
booktitle={Proceedings of the 17th International Conference on Agents and Artificial Intelligence - Volume 2: ICAART},
year={2025},
pages={586-597},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0013243100003890},
isbn={978-989-758-737-5},
}

in EndNote Style

TY - CONF

JO - Proceedings of the 17th International Conference on Agents and Artificial Intelligence - Volume 2: ICAART
TI - Multi-Agent Trajectory Prediction for Urban Environments with UAV Data Using Enhanced Temporal Kolmogorov-Arnold Networks with Particle Swarm Optimization
SN - 978-989-758-737-5
AU - Mohebbi M.
AU - Kafash E.
AU - Döller M.
PY - 2025
SP - 586
EP - 597
DO - 10.5220/0013243100003890
PB - SciTePress