Route Evaluation Models Using Pilot Feedback for Drone Flight Suggestions
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Solution Overview
Problem
Existing drone flight systems, particularly under Visual Flight Rules (VFR), lack the ability to register flight plans during maneuvers and require external systems to suggest flight routes tailored to pilot characteristics for enhanced safety.
Innovation Solution
A model generation device that generates and evaluates suggested routes based on pilot feedback, using a route evaluation model trained with user evaluations to adapt route suggestions to individual pilot characteristics.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If an external system suggests flight routes to enhance safety, then flight safety is improved, but the system complexity increases
Solution Approach 1:
The system acquires user evaluations of suggested routes and uses this feedback to generate training data, which is then used to generate personalized route evaluation models. This feedback loop enables the system to learn from user preferences and improve route suggestions over time, enhancing safety while managing complexity through adaptive learning rather than complex rule-based systems
Solution Approach 2:
The system automatically generates training data from user evaluations and autonomously creates personalized route evaluation models without requiring manual programming of pilot characteristics. This self-service approach reduces the need for complex manual configuration while still achieving personalized safety enhancements
2Adaptability or versatility
If route suggestions are personalized to pilot characteristics, then adaptability is improved, but the device complexity increases
Solution Approach 1:
The system uses user evaluations as feedback to automatically generate training data and personalize route evaluation models. This feedback mechanism enables the system to adapt to individual pilot characteristics through learning rather than complex manual analysis, achieving personalization with manageable system complexity
Solution Approach 2:
The system changes the approach from static rule-based personalization to dynamic model-based personalization. By generating training data from user evaluations and creating personalized models, the system adapts to different pilot characteristics through parameter learning rather than complex structural changes, improving adaptability while controlling complexity
Data Source
AI summary
In a model generation device, a proposed route generation means generates and outputs a suggested route indicating a traveling route of a moving object. An evaluation acquisition means acquires an evaluation of a user for the suggested route. A training data generation means generates training data using the evaluation of the user which is acquired. A model generation means generates a route evaluation model indicating a relationship between the traveling route and the evaluation of the user, using the training data.