Pretraining of Autoregressive Model for Aircraft Hard Landing Prediction Based on QAR Data A hard landing occurs when an aircraft or spacecraft hits the ground with a greater vertical speed and force than in a normal landing, which can cause serious damage to the aircraft and even put passengers in great danger. Predicting the hard landing ahead is valuable, however, it has not been fully investigated. Existing studies mainly apply statistical machine learning algorithms with feature engineering on smallscale data sets. Such methods are primarily dependent on constructing features by experts. In addition, since hard landing samples are difficult to collect, existing research only use a smallscale training sets, which limits the capacity of models. In this paper, we explore a novel autoregressive pretraining approach for hard landing prediction using its flight parameters before touchdown provided by quick access recorder (QAR). QAR is an airborne flight data recorder that records more than 1000 flight parameters throughout the whole flight phase. Large amount of landing data from QAR, which can be considered as multivariate time series, were processed and converted to symbolic representations. Each token represents one landing status in a landing time series. An Aircraft Landing Model (ALM) is pretrained on these landing token sequences in an autoregressive way that predicts the next landing status according to the previous landing status. The pretrained ALM can learn the transition patterns between landing status in a normal landing and automatically extract useful features to characterize a landing process. Finally, the pretrained ALM model is finetuned on hard landing training set to predict hard landing. Experimental results show that our approach significantly improved FScore of hard landing prediction by 14% compared to other methods.

Bayesian Neural Networks for Flight Trajectory Prediction and Safety Assessment Abstract   Safety, as the most important concern in civil aviation, needs to be maintained at an acceptable level at all times in the air transportation system. This paper aims to increase enroute flight safety through the development of deep learning models for trajectory prediction, where model prediction uncertainty is characterized following a Bayesian approach. The proposed methodology consists of four steps. In the first step, a large volume of raw messages in Flight Information Exchange Model (FIXM) format streamed from Federal Aviation Administration are processed with a distributed computing engine Apache Spark to extract trajectory information in an efficient manner. In the second step, two types of deep learning models are trained to predict flight trajectory from different perspectives. Specifically, deep feedforward neural networks (DNN) are trained to make a onestepahead prediction on the deviation along latitude and longitude between the actual flight trajectory and target flight trajectory. In parallel, deep Long ShortTerm Memory (LSTM) neural networks are trained to make longerterm predictions on the flight trajectory over several subsequent time instants. The DNN model is more accurate but has a singlestep prediction horizon, whereas the LSTM model is less accurate but longer prediction horizon. Therefore, in the third step, the two different types of deep learning models are blended together to create a multifidelity prediction. After quantifying the discrepancy between the two model predictions in the current time instant, the DNN prediction is used to correct the LSTM prediction of flight trajectory along subsequent time instants accordingly. The multifidelity approach is expanded to multiple flights, and is then used to assess safety based on horizontal and vertical separation distance between two flights. Computational results illustrate the promising performance of the blended model in predicting the flight trajectory and assessing enroute flight safety.