A Novel Approach on Vessel Trajectory Prediction Based on Variational LSTM

With the increasing complexity of maritime traffic, vessel trajectory prediction becomes a vital part of the maritime security. Although many methods working on this problem have been proposed including machine learning methods, physical model methods and deep learning methods. But all these methods can not take long-time prediction and multidimensional prediction in to account at the same time. Besides, even some methods avoided the curved trajectory prediction deliberately. In this paper we investigated a novel approach based on Variational LSTM to predict the vessel trajectory using Automatic Identification System (AIS) data. The predicted time scope in ranges from about 5 minutes to about 20 minutes and the predicted features including time, longitude, latitude, speed over ground (SOG) and course over ground (COG). To avoid damaging the structure of the raw AIS data, we have not used any interpolation method when processed it. Instead, we calculated the time increment between two continuous data points. Then we used a novel method based on time increment to select out those trajectory segments with preferable time continuity to make dataset. Lastly, in long-time and multidimensional prediction experiments, variational LSTM achieved the better results and showed the potential in the long-time trajectory prediction comparing with the LSTM. In 20-time-steps position prediction, the MSE of variational LSTM was only 6.0303e-4 but that value of LSTM reached to 6.0014e-3.