Using AI Copilots to Enhance Aviation Safety for Autonomous Aircraft Operations

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[...] Things will inform the refinement and validation of the system, ultimately contributing to enhanced aviation safety standards for autonomous aircraft operations. Work Cited Asokan, Aravind, and Bruce G Cameron. "Single-Pilot Aircraft in Commercial Air Transport Operations: A Comparison of Potential Architectures." Journal of Air Transportation, vol no July 2023, pp. 113-127, https://doi.org/10.2514/1.d0340. It was accessed on 25 Apr. 2024. Emha Abdillah, Risya, et al. "Implementation of Artificial Intelligence on Air Traffic Control - a Systematic Literature Review." 2024 18th International Conference on Ubiquitous Information Management and Communication (IMCOM) Jan https://doi.org/10.1109/imcom60618.2024.10418350. [...]

[...] The aviation authorities and the governing body in charge of deploying AI must work together and provide clear definitions for continuous monitoring, incident investigations and policy updates. Harmonizing AI governance across international aviation bodies ensures a smooth global operation. Ultimately, overall public approval depends on demonstrating AI's safety advantages, ethical deployment protocols, and general agreement to handle failure. This study guides the core ethical AI principles such as accountability, transparency, and human supervision, along with engaging regulators for the integrated copilot of AI, therefore assuring the aviation safety standards are improved. [...]

[...] Objectives and Scope This research seeks to develop an AI copiloting model to boost safety standards for autonomous aircraft. Specific objectives include enhancing risk assessment, determining countermeasures, and human-AI cooperation in routine and abnormal situations. Safety metrics relate to measuring safety gains for automated operations against baseline (conventional) methods. Hypotheses The central hypothesis is that an AI copilot system can enhance aviation safety metrics (e.g. reduced incidents, improved response times) for autonomous aircraft operations through: - Intelligent data analysis and predictive modelling for superior risk assessment and mitigation compared to conventional methods. [...]

[...] Evaluation metrics of AI co-pilot system performance will include safety index quantifiable measure of safety performance), human workload (qualitative and quantitative indices), situational awareness, and subjective assessments (description of model's usability, trust, transparency and general rating). Statistical methods, such as ANOVA and regression models, will be used to determine the importance of the results and spot crucial factors affecting the system's performance. Ethics The inception of AI copilots in unmanned aviation entails muscular ethical and rule issues that must be given due attention. [...]

[...] These diverse AI components will be implemented with a BDI-based control architecture to create situation awareness using multi-modal data fusion and reasoning. The predictive analysis will employ the deep learning technique of LSTM operating on the time series data from multiple sensors. To achieve this, the system will use reinforcement learning methods like deep Q-networks (DQN) and Monte Carlo tree search (MCTS). In-depth simulation testing will occur by embedding the AI system in flight simulation environments like X-Plane and Flight Gear. [...]