Deep Learning in Unmanned Surface Vehicles Collision-Avoidance Pattern Based on AIS Big Data with Double GRU-RNN

Jia-Hui Shi,Zheng-Jiang Liu

doi:10.3390/jmse8090682

Jia-Hui Shi, Zheng-Jiang Liu

Open Access

https://doi.org/10.3390/jmse8090682

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Abstract

There is a collection of a large amount of automatic identification system (AIS) data that contains ship encounter information, but mining the collision avoidance knowledge from AIS big data and carrying out effective machine learning is a difficult problem in current maritime field. Herein, first the Douglas–Peucker (DP) algorithm was used to preprocess the AIS data. Then, based on the ship domain the risk of collision was identified. Finally, a double-gated recurrent unit neural network (GRU-RNN) was constructed to learn unmanned surface vehicle (USV) collision avoidance decision from the extracted data of successful encounters of ships. The double GRU-RNN was trained on the 2015 Tianjin Port AIS dataset to realize the effective learning of ship encounter data. The results indicated that the double GRU-RNN could effectively learn the collision avoidance pattern hidden in AIS big data, and generate corresponding ship collision-avoidance decisions for different maritime navigation states. This study contributes significantly to the increased efficiency and safety of sea operations. The proposed method could be potentially applied to USV technology and intelligence collision avoidance.

Highlights

With the development and the integration of the global economy, the marine world has become an important link for transportation and trade development in all countries in the world
The automatic identification system (AIS) data used in this article was the full-year AIS data of the Tianjin Port in 2015, including
The AIS data used in this article was the full-year AIS data of the Tianjin Port in 2015, including the 22,349 ships occupying 8.3 GB of storage space, with the 1,511,504,900 trajectory points

Summary

Introduction

With the development and the integration of the global economy, the marine world has become an important link for transportation and trade development in all countries in the world. The frequent occurrence of collisions at sea has caused great losses in terms of human lives and property and has a wide coverage, posing a serious threat to marine ecology and the environment. The development of unmanned ships has become an inevitable trend in future ship development Due to their small size and intelligence, unmanned surface vehicles (USV) are often used to perform specific tasks, such as maritime rescue, marine surveying, and monitoring of dangerous goods.

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