@article{Momani2026, 
author = {Alaa M. Momani and Deema Mohammed Alsekait and Mahmoud Ahmad Al-Khasawneh and Siti Hajar Othman and Ibraheem Al-Tarawneh and Nikunj Sharma and Wee How Khoh},
title = {Generative World Modeling for Risk-Aware Autonomous UAV Navigation in Dynamic Traffic Networks},
year = {2026},
journal = {Computers, Materials & Continua},
volume = {88},
number = {3},
pages = {68},
keywords = {Unmanned aerial vehicles, generative AI, autonomous navigation, risk-aware decision, trajectory planning, swarm coordination},
url = {https://www.sciopen.com/article/10.32604/cmc.2026.082572},
doi = {10.32604/cmc.2026.082572},
abstract = {Unmanned Aerial Vehicles (UAVs) are finding more and more applications in logistics, surveillance, and other operations at a large scale. However, autonomous navigation in dynamic traffic situations is not an easy task due to limited energy, moving obstacles, and inter-agent interactions. The proposed paper can be discussed as a Generative World Modeling (GWM) framework of risk-focused UAV navigation in the dynamic traffic network. This paper proposes a GWM framework for risk-aware UAV navigation in dynamic traffic networks. The proposed design incorporates three key elements; a generative world model for predicting future environmental conditions, a diffusion-based trajectory-generation component that generates multiple possible paths, and a risk-aware decision-making component that selects trajectories based on energy use, collision avoidance, and mission criteria. The framework is also extended to the case of a multi-UAV swarm, where a coordinated swarm is facilitated by shared representations in the latent space to alleviate potential conflicts. The experimental analysis of real-world-inspired UAV trajectory data indicates that the proposed GWM framework outperforms the classical, reinforcement-based, and conflict-aware baseline approaches across a range of performance metrics, including mission success rate, delivery time, energy cost, safety, and path efficiency. The findings indicate that with risk-sensitive and generative prediction, autonomous UAV missions should be more robust, effective, and secure in uncertain, complex environments.}
}