Intelligent systems for aviation need to be capable of understanding and representing anomalous events as they happen in real-time. We explore this problem with a proof of concept framework based on contextual one-shot learning, run on a human-in-the-loop flight simulator. We ran a total of 24 trials, with variations in training, fliers, and set values within the framework, and found that our framework was able to detect and reason about anomalies in all trials. In future work, we would like to explore different heuristics for anomaly reasoning, including nonlinear interactions of cockpit data, and feedback from the flight crew through psychophysiology sensors or natural language interactions.
Knowledge Acquisition in the Cockpit Using One-Shot Learning
01.07.2018
2252008 byte
Aufsatz (Konferenz)
Elektronische Ressource
Englisch
Springer Verlag | 2019
|Springer Verlag | 2017
|Aircraft cockpit video acquisition and recording system configuration
Europäisches Patentamt | 2024
|Kraftfahrwesen | 1999
|Springer Verlag | 2023
|