Explainable AI and Monocular Vision for Enhanced UAV Navigation in Smart Cities: Prospects and Challenges

Shumaila Javaid ¹ Muhammad Asghar Khan ² Hamza Fahim ¹ Bin He ¹ Nasir Saeed ^3*

• ¹ Tongji University, Shanghai, Shanghai Municipality, China
• ² Prince Mohammad bin Fahd University, Khobar, Saudi Arabia
• ³ United Arab Emirates University, Al-Ain, Abu Dhabi, United Arab Emirates

Explainable Artificial Intelligence (XAI) is increasingly pivotal in Unmanned Aerial Vehicle (UAV) operations within smart cities, enhancing trust and transparency in AI-driven systems by addressing the 'black-box' limitations of traditional Machine Learning (ML) models. This paper provides a comprehensive overview of the evolution of UAV navigation and control systems, tracing the transition from conventional methods such as GPS and inertial navigation to advanced AI-and ML-driven approaches. It investigates the transformative role of XAI in UAV systems, particularly in safety-critical applications where interpretability is essential. A key focus of this study is the integration of XAI into monocular vision-based navigation frameworks, which, despite their cost-effectiveness and lightweight design, face challenges such as depth perception ambiguities and limited fields of view. Embedding XAI techniques enhances the reliability and interpretability of these systems, providing clearer insights into navigation paths, obstacle detection, and avoidance strategies. This advancement is crucial for UAV adaptability in dynamic urban environments, including infrastructure changes, traffic congestion, and environmental monitoring. Furthermore, this work examines how XAI frameworks foster transparency and trust in UAV decision-making for high-stakes applications such as urban planning and disaster response. It explores critical challenges, including scalability, adaptability to evolving conditions, balancing explainability with performance, and ensuring robustness in adverse environments. Additionally, it highlights the emerging potential of integrating vision models with Large Language Models (LLMs) to further enhance UAV situational awareness and autonomous decision-making. Accordingly, this study provides actionable insights to advance next-generation UAV technologies, ensuring reliability and transparency. The findings underscore XAI's role in bridging existing research gaps and accelerating the deployment of intelligent, explainable UAV systems for future smart cities.