%0 Journal Article
%A HUANG Feixiang
%A JIA Haipeng
%A LI Xujuan
%A PI Jianyong
%T Self-generated deep neural network based 4D trajectory prediction
%D 2021
%R 10.11772/j.issn.1001-9081.2020081198
%J Journal of Computer Applications
%P 1492-1499
%V 41
%N 5
%X Since 4-Dimensional (4D) trajectory prediction is not real-time and has the iterative error, an Automatically generated Conditional Variational Auto-Encoder (AutoCVAE) was proposed. It is in the form of encoding-decoding to predict the future trajectory directly, and can select observation number and prediction step flexibly. The method was guided by the preprocessed Automatic Dependent Surveillance-Broadcast (ADS-B) data, and with the reduction of the prediction error as the goal. By means of Bayesian optimization, the model structure was searched within the predefined search space. The hyper parameter values of each time were chosen by referencing the previous evaluation results, so that the structure of the new model obtained in each time was able to be closer to the target, and ultimately, a high precision 4D trajectory prediction model based on ADS-B data was completed. In the experiments, the proposed model was able to predict the trajectory quickly and accurately in real time with the Mean Absolute Error (MAE) of both latitude and longitude less than 0.03 degrees, the altitude MAE under 30 m, the time error at each time point not exceeded 10 s, and each batch trajectory prediction delay within 0.2 s.
%U http://www.joca.cn/EN/10.11772/j.issn.1001-9081.2020081198