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

Mohammad Reza Mohebbi, Elahe Kafash, Mario Döller

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 predic

External IDs:dblp:conf/icaart/MohebbiKD25