Regression Methods Applied to Flight Variables for Situational Awareness Estimation Using Dynamic Bayesian Networks

Situational awareness can be a valuable indicator of the performance of flight crews and the way pilots manage navigation information can be relevant to its estimation. In this research, dynamic Bayesian networks are applied to a dataset of variables both collected in real time during simulated flights and added with expert knowledge. This paper compares different approaches to the discretization of continuous variables and to the estimation of pilot actions based on variable regression, in order to optimize the model performance.