@Article{cmes.2026.085610,
AUTHOR = {Rihem Farkh, Ghislain Oudinet, Alaeddine Moussa, Yasser Fouad},
TITLE = {Predictive and Explainable UAV Navigation via World Models, Structured Future Reasoning and LLM-Based Explanation},
JOURNAL = {Computer Modeling in Engineering \& Sciences},
VOLUME = {},
YEAR = {},
NUMBER = {},
PAGES = {{pages}},
URL = {http://www.techscience.com/CMES/online/detail/28317},
ISSN = {1526-1506},
ABSTRACT = {Autonomous UAV navigation in safety-critical environments requires more than accurate prediction of future states; it also requires the ability to interpret action-conditioned futures as actionable safety risk. This paper presents a predictive and explainable UAV navigation framework that combines a latent world model with a Future Interpretation Module (FIM), a safety-constrained decision layer, and a hybrid explanation module. The world model predicts action-conditioned latent futures, while the FIM converts these rollouts into interpretable descriptors including time-to-collision, minimum vertical clearance, route-commitment risk, and risk trend. These descriptors are used jointly for decision-making, safety filtering, and explanation grounding. In a delayed-hazard diagnostic benchmark with 150 episodes per agent, the FIM policy achieved 100.0% success and 0.0% collisions, compared with 97.3% success and 2.7% collisions for the prediction-only world-model baseline. In descriptor-level ablation, Full FIM achieved the highest success rate (71.3%) while eliminating collisions and route traps, whereas individual descriptors reduced collisions but often produced route-trap failures. Across six cross-environment validation scenarios and 900 Full-FIM episodes, the method achieved 98.0% success and 0.0% collisions. Because the prediction-only baseline was also near saturation in this broad suite, these cross-environment results are interpreted as robustness evidence rather than as a claim of uniform superiority. Overall, the results show that FIM improves diagnostic delayed-risk reasoning and provides robust collision-free behavior across the evaluated suite, while broad superiority over all prediction-only baselines remains outside the scope of the current evidence. Secondary comparisons with adapted YUME, Matrix-Game, NWM, and ANWM remain diagnostic references under a unified three-action UAV interface, not complete re-evaluations of those methods in their native settings.},
DOI = {10.32604/cmes.2026.085610}
}