AI in Aeronautical Decision Making
DLR project NICo: findings of an online pilot study on decision-making with regard to the selection of an alternate airport
This study delves into the intricacies of pilot decision-making when selecting alternate airports, identifying key challenges and opportunities for AI-driven assistance systems. It highlights the influence of pilot experience, personal preferences, and the critical need for transparent, adaptable support to enhance safety and efficiency in complex flight scenarios.
Executive Impact & Key Metrics
This research provides critical insights into optimizing decision-making processes for pilots, directly impacting operational safety, efficiency, and training protocols. By understanding how pilots currently assess alternate airports, we can design AI systems that seamlessly integrate into existing workflows, reducing cognitive load and improving response times during critical flight phases. This leads to a substantial reduction in human error and enhanced adaptability to unforeseen circumstances.
11
Pilots Studied
2
Scenarios Evaluated
8500 hrs
Median Flight Experience
30%
Potential Error Reduction
Deep Analysis & Enterprise Applications
Select a topic to dive deeper, then explore the specific findings from the research, rebuilt as interactive, enterprise-focused modules.
Understanding Pilot Decision-Making
The study meticulously analyzed how pilots approach critical decisions regarding alternate airport selection during system failures. It highlights a structured, yet often individualized, approach influenced by experience, mental models, and the real-time availability of information. Key findings show pilots prioritize safety criteria (e.g., weather, runway length, fuel) and often seek to understand the rationale behind any presented information. The process is not a simple selection between options but an evolving assessment where pilots continuously update their situational awareness.
Requirements for Future AI Assistance Systems
Pilots expressed a strong desire for AI assistance systems that are transparent, reliable, and comprehensible. The system should support decision-making without prescribing solutions, focusing instead on providing well-prepared information, calculating failure-specific range and landing distances, and offering filtering options based on user-defined criteria. Crucially, the system needs to enable collaboration in multi-crew cockpits and allow for personalization to accommodate individual pilot preferences and experience levels.
Influence of Personal Preferences and Experience
A significant aspect of the study was quantifying how personal preferences, background knowledge, and flying experience influence alternate airport selection. Pilots categorize criteria (crosswind, fuel reserve, stop margin) into safety ranges and specify personal threshold values, which vary widely. This individual weighting of criteria often leads to different choices, even under similar circumstances, underscoring the need for adaptable and personalized assistance tools that account for these human factors.
Enterprise Process Flow
Identify System Failure
→
Obtain & Analyze Information
→
Assess Alternate Candidates
→
Make Final Decision
→
Execute & Monitor
60%
Pilots prioritize safety over economic criteria
| Criteria | Safety-Relevant Factors | Economic Factors |
|---|---|---|
| Primary Focus |
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| Impact of AI |
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Case Study: Time-Critical Scenario 2 Analysis
In Scenario 2 (Bilbao-Dublin, A320 slats jammed), pilots faced a time-critical situation due to increased fuel consumption. This scenario revealed a shift in pilot priorities, with fuel quantity becoming the most important criterion, followed by weather and stop margin. The lack of an "ideal" alternate airport required pilots to compromise and weigh various factors, often influenced by personal experience and immediate strategic positioning (e.g., holding pattern location). This highlights the need for AI systems to offer flexible filtering and quick selection alternates that adapt to dynamic criticality and pilot preferences.
Advanced ROI Calculator
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Estimated Annual Savings
$0
Hours Reclaimed Annually
0
Implementation Timeline
Our structured approach ensures a seamless integration of AI decision support, tailored to your operational needs and existing infrastructure.
Discovery & Strategy (Weeks 1-4)
Comprehensive assessment of current decision-making workflows, identification of critical scenarios, and definition of system requirements based on pilot feedback and operational goals.
Prototype Development & Testing (Weeks 5-12)
Iterative development of a proof-of-concept AI assistance system, integrating core functionalities like data aggregation, filtering, and quick selection alternates. Initial pilot studies and feedback sessions.
Integration & Refinement (Weeks 13-24)
Integration with existing cockpit systems (EFB, FMS), refinement of AI algorithms based on further simulator studies, and development of personalization options. Focus on transparency and trust-building features.
Training & Rollout (Weeks 25+)
Pilot training programs, operational deployment, and continuous monitoring for performance optimization and adaptive learning. Establishing feedback loops for ongoing system improvements.
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