Spatial Situational Awareness for Safe UAVs in XAI-enhanced 6G Networks: Vision, Challenges, and Opportunities

Abstract

Spatial situational awareness represents a key enabler for the safe and effective integration of uncrewed aerial vehicles (UAVs) into sixth generation (6G) networks, addressing the challenges of operating in complex and dynamic environments. As part of the broader vision for integrated terrestrial/nonterrestrial networks (T/NTNs), UAVs contribute to a 3D wireless connectivity infrastructure alongside ground networks, high altitude platforms (HAPs), and satellites. However, ensuring trustworthy operations requires UAVs to navigate with precision in an environment affected by occlusions, network coverage variability, and unpredictable weather conditions. Artificial intelligence (AI), a native element of 6G, offers robust capabilities to tackle these challenges. Nevertheless, the opacity of deep learning (DL) models introduces trust and reliability concerns, particularly in safetycritical missions. Explainable AI (XAI) addresses this gap by enhancing transparency and enabling informed decision-making at all mission stages. Through XAI-driven frameworks, UAVs would employ high-resolution geospatial data, network coverage maps, and weather forecasts to plan and adapt their trajectories dynamically, ensuring transparent decision-making for spatial safety and mission success. To this end, this work introduces UAV spatial situational awareness as a foundational component for the safe integration of drones in the wireless 6G infrastructure. It explores the integration of XAI into mission planning and realtime operations, ensuring reliable navigation and safe operations even in challenging environments. The study introduces the concept, investigates key challenges, and provides insights into its potential across innovative use cases, including flying base stations, emergency response, and last-mile delivery.

Cite this work

@misc{grieco2025spatial,
  title = {{Spatial Situational Awareness for Safe UAVs in XAI-enhanced 6G Networks: Vision, Challenges, and Opportunities}},
  author = {Giovanni Grieco and Arcangela Rago and Gennaro Boggia and Luigi Alfredo Grieco},
  howpublished = {TechRxiv (preprint)},
  year = {2025},
  doi = {10.36227/techrxiv.175695641.10388179/v1},
  url = {https://doi.org/10.36227/techrxiv.175695641.10388179/v1}
}

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