Open Access

ARTICLE

Generative World Modeling for Risk-Aware Autonomous UAV Navigation in Dynamic Traffic Networks

Alaa M. Momani¹, Deema Mohammed Alsekait², Mahmoud Ahmad Al-Khasawneh^1,*, Siti Hajar Othman³, Ibraheem Al-Tarawneh⁴, Nikunj Sharma⁵, Wee How Khoh⁶

1 School of Computing, Horizon University College, Ajman, United Arab Emirates
2 Department of Information Technology, College of Computer and Information Sciences, Princess Nourah bint Abdulrahman University, Riyadh, Saudi Arabia
3 Faculty of Computing, Universiti Teknologi Malaysia, Johor Bahru, Johor, Malaysia
4 Mechanical and Industrial Engineering Department, Faculty of Engineering, Hourani Center for Applied Scientific Research, Al-Ahliyya Amman University, Amman, Jordan
5 Data Engineer, Amazon, Seattle, WA, USA
6 Center for Advanced Analytics, CoE for Artificial Intelligence, Faculty of Information Science & Technology (FIST), Multimedia University, Jalan Ayer Keroh Lama, Bukit Beruang, Melaka, Malaysia

* Corresponding Author: Mahmoud Ahmad Al-Khasawneh. Email:

(This article belongs to the Special Issue: Integrating Generative AI with UAVs for Autonomous Navigation and Decision Making)

Computers, Materials & Continua 2026, 88(3), 68 https://doi.org/10.32604/cmc.2026.082572

Received 18 March 2026; Accepted 14 May 2026; Issue published 23 July 2026

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.

---

Keywords

---