Analysis of Single-Pilot Intention Modeling in Commercial Aviation

Abstract

With the continual enhancement of the onboard avionics, the minimum flight crew has been downsized from five to two-person crew mode, and reduced crew operation has drawn extensive attention from aviation experts. Single-pilot operation (SPO) mode warrants careful account and research. This study investigated the intention modeling of commercial aviation single pilot based on the bidirectional long short-term memory (BiLSTM), mining the intention tendency of pilots' behavior through artificial intelligence technology. This was done to avoid safety hazards caused by different intents and inconsistent operations of the single pilot and the cockpit automation system. The classification task of a single pilot's behavior is the core of intention recognition. Various operation items contribute differently to the classification. To construct the interaction dataset and encode it into time series features, a single-pilot experiment is specifically performed, wherein the experience of an expert is summarized into single-pilot intent labels. The deep information in the feature vector of a single-pilot operation item is captured by the BiLSTM network, and the neural weight is adaptively assigned by the training mechanism. The operation sequence with the feature data is finally loaded into the softmax layer for intention classification. The proposed method is evaluated against long short-term memory (LSTM), term frequency-inverse document frequency (TF-IDF), convolutional neural network (CNN), Naive Bayesian (NB), and distributed representation's intention modeling techniques. Because the proposed methods have higher F1 scores, the model can effectively share real-time information about the single-pilot intention with the cockpit automation system.