arXiv:2502.10764v4 Announce Type: replace Abstract: Understanding how air traffic controllers construct a mental 'picture' of complex air traffic situations is crucial but remains a challenge due to the inherently intricate, high-dimensional interactions between aircraft, pilots, and controllers. Previous work on modeling the strategies of air traffic controllers and their mental image of traffic situations often centers on specific air traffic control tasks or pairwise interactions between aircraft, neglecting to capture the comprehensive dynamics of an air traffic situation. To address this
The increasing complexity of air traffic systems and the maturation of AI models necessitate advanced solutions for understanding and explaining complex operational environments.
Improving AI's ability to 'explain' complex situations like air traffic control is crucial for deploying autonomous or semi-autonomous systems in safety-critical domains, fostering trust and operational efficiency.
This research moves towards AI systems that can not only predict or optimize but also interpret and communicate the reasoning behind their understanding of a complex, dynamic environment.
- · Air Traffic Control organizations
- · AI/ML researchers in explainable AI
- · Aerospace industry
- · Aviation safety regulators
- · Systems reliant solely on human interpretation of complex data
AI models gain enhanced capabilities in interpreting and explaining high-dimensional, real-time interactive systems.
Increased adoption of AI-assisted decision-making tools in air traffic management due to improved explainability and trust.
Potential for fully autonomous air traffic control systems capable of managing increasingly complex airspaces with improved safety and efficiency.
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Read at arXiv cs.LG