Citation

Momani, Alaa M. and Alsekait, Deema Mohammed and Al-Khasawneh, Mahmoud Ahmad and Othman, Siti Hajar and Al-Tarawneh, Ibraheem and Sharma, Nikunj and Khoh, Wee How (2026) Generative World Modeling for Risk-Aware Autonomous UAV Navigation in Dynamic Traffic Networks. Computers, Materials & Continua, 88 (3). pp. 1-10. ISSN 1546-2226

Text Generative World Modeling for Risk-Aware Autonomous UAV Navigation in Dynamic Traffic Networks.pdf - Published Version Restricted to Repository staff only Download (3MB)

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 diffusionbased 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.

Item Type:	Article
Uncontrolled Keywords:	Unmanned aerial vehicles, generative AI
Subjects:	T Technology > TL Motor vehicles. Aeronautics. Astronautics > TL1-484 Motor vehicles. Cycles
Divisions:	Faculty of Information Science and Technology (FIST)
Depositing User:	Ms Rosnani Abd Wahab
Date Deposited:	04 Sep 2026 02:15
Last Modified:	04 Sep 2026 02:15
URII:	http://shdl.mmu.edu.my/id/eprint/16680

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