Recent advances in Physical AI have accelerated the use of foundation models in autonomous systems such as unmanned aerial vehicles (UAVs), which must perceive, reason, plan, and act in dynamic environments. Existing benchmarks assess physical perception, intuitive physics, embodied navigation, and collaborative reason...
M. Ferrag, Mérouane Debbah, Abderrahmane Lakas et al.· 0 citations
Autonomous aerial systems increasingly rely on large language models (LLMs) for mission planning, perception, and decision-making; yet, the lack of standardized, physically grounded benchmarks limits systematic evaluation of their reasoning capabilities. To address this gap, we introduce UAVBench, an open benchmark dat...
M. Ferrag, Abderrahmane Lakas, Mérouane Debbah· IEEE Open Journal of Vehicul...· 15 citations
MulRobBench provides a reproducible benchmark for trustworthy multimodal UAV decision making under realistic operational constraints and identifies modality-trust selection, constraint extraction, glare, missing data, and operator shorthand as the primary causes of decision instability.
B. Alsinglawi, Wei-Zheng Wang, Jun-Yi Wu et al.· arXiv.org· 0 citations
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