AI Pilot Assistance Guidance for Aircraft Emergency Decisions
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Solution Overview
Problem
Emergency situations in aircraft pose challenges for pilots due to the lack of real-time cross-validation of their actions, absence of forecast or visualization of action spaces, and uncertainty about outcomes, with existing systems lacking guarantees for safety and performance.
Innovation Solution
An artificial intelligence-powered emergency pilot assistance system trained in an aircraft simulator computes velocities, altitudes, and headings, providing suggestive guidance through an artificial neural network that calculates reward values for optimal actions, including a deep Q network trained in two phases with pilot and automated scenarios.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If an AI-powered emergency pilot assistance system is implemented to provide real-time guidance and cross-validation, then pilot decision-making is enhanced and safety is improved, but system complexity increases
Solution Approach 1:
The patent introduces an AI-powered assistant system as an intermediary between the pilot and the aircraft control systems. This assistant provides real-time cross-validation of pilot actions, suggests optimal maneuvers, and forecasts action spaces without directly controlling the aircraft. The system acts as a cognitive mediator that enhances pilot decision-making while maintaining pilot authority, thus improving safety without requiring full automation.
Solution Approach 2:
The system implements continuous feedback loops by monitoring aircraft state, comparing pilot actions against trained AI models, and providing real-time suggestions and cross-validation. The AI model is itself trained using feedback from simulated emergency scenarios, creating a closed-loop system that continuously improves its guidance capabilities while providing real-time feedback to the pilot during emergencies.
2Loss of information
If real-time cross-validation and forecast of action space are provided to the pilot, then pilot uncertainty is reduced and decision-making is improved, but information processing requirements and computational load increase
Solution Approach 1:
The AI model is pre-trained offline using extensive simulation data covering various emergency scenarios. This preliminary training phase allows the system to learn optimal responses and action spaces before deployment. During actual emergency operations, the pre-trained model can quickly process current aircraft state and provide guidance without requiring heavy real-time computation, thus reducing operational computational load while maintaining comprehensive decision-support capabilities.
3Ease of operation
If the system provides comprehensive real-time guidance and action recommendations, then pilot workload is reduced during emergencies, but the system requires extensive training data and processing time
Solution Approach 1:
The system performs extensive training in advance using simulated emergency scenarios. The AI model is trained offline on large datasets of simulated flight situations, allowing it to develop robust decision-making capabilities before actual deployment. This preliminary training ensures the system is ready to provide immediate guidance during real emergencies without requiring time-consuming processing during critical moments.
Solution Approach 2:
The system uses simulated flight data and virtual training scenarios to create training datasets without requiring actual emergency flights. By copying and analyzing simulated emergency situations, the system can be trained extensively on diverse scenarios without consuming real flight time or exposing aircraft to actual danger, thus reducing the time and resources needed for training while maintaining comprehensive operational readiness.
Data Source
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AI summary
An emergency pilot assistance system (300) may include an artificial neural network (338) configured to calculate reward (Q) values (352) based on state-action vectors (332) associated with an aircraft (302). The state-action vectors (332) may include state data (334, 500) associated with the aircraft (302) and action data (336, 600) associated with the aircraft (302). The system (300) may further include a user output device (310) configured to provide an indication (312) of an action (314) to a user (324), wherein the action (314) corresponds to an agent action (366) that has a highest reward Q value (368) as calculated by the artificial neural network (338).