An improved AIS trajectory prediction model based on TrAISformer

Jinwei Cheng; Junrui Wang; Zheng Zhang; Jie Yuan; Wenbo Shao

doi:10.1117/12.3015737

20 February 2024 An improved AIS trajectory prediction model based on TrAISformer

Jinwei Cheng, Junrui Wang, Zheng Zhang, Jie Yuan, Wenbo Shao

Proceedings Volume 13064, Seventh International Conference on Traffic Engineering and Transportation System (ICTETS 2023); 1306410 (2024) https://doi.org/10.1117/12.3015737
Event: 7th International Conference on Traffic Engineering and Transportation System (ICTETS 2023), 2023, Dalian, China

Abstract

As a key technology for maritime applications, trajectory prediction can effectively help ships reduce risks such as collisions and groundings at sea. Currently, although the combination of rich automatic identification system (AIS) data and deep learning brings new possibilities for ship trajectory prediction, ship trajectory prediction is still hugely challenging due to the complexity of ship motion. In this paper, we improved a new ship prediction model based on TrAISformer. On the one hand, we sparse the multi-dimensional data through dictionary coding, map it into probability space, and use a new loss function to measure network performance. On the other hand, we proposed a An MLP algorithm enables it to effectively learn the timing characteristics of AIS data while avoiding destroying encoding correlation. Finally, we conducted experiments on public AIS data, and the experiments showed that the improved model improved the prediction performance by about 12.5 % compared with TrAISformer.

(2024) Published by SPIE. Downloading of the abstract is permitted for personal use only.

Citation Download Citation

Jinwei Cheng, Junrui Wang, Zheng Zhang, Jie Yuan, and Wenbo Shao "An improved AIS trajectory prediction model based on TrAISformer", Proc. SPIE 13064, Seventh International Conference on Traffic Engineering and Transportation System (ICTETS 2023), 1306410 (20 February 2024); https://doi.org/10.1117/12.3015737

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