Ship-Collision Avoidance Decision-Making Learning of Unmanned Surface Vehicles with Automatic Identification System Data Based on Encoder—Decoder Automatic-Response Neural Networks

Guo-You Shi,Miao Gao

doi:10.3390/jmse8100754

Abstract

Intelligent unmanned surface vehicle (USV) collision avoidance is a complex inference problem based on current navigation status. This requires simultaneous processing of the input sequences and generation of the response sequences. The automatic identification system (AIS) encounter data mainly include the time-series data of two AIS sets, which exhibit a one-to-one mapping relation. Herein, an encoder–decoder automatic-response neural network is designed and implemented based on the sequence-to-sequence (Seq2Seq) structure to simultaneously process the two AIS encounter trajectory sequences. Furthermore, this model is combined with the bidirectional long short-term memory recurrent neural networks (Bi-LSTM RNN) to obtain a network framework for processing the time-series data to obtain ship-collision avoidance decisions based on big data. The encoder–decoder neural networks were trained based on the AIS data obtained in 2018 from Zhoushan Port to achieve ship collision avoidance decision-making learning. The results indicated that the encoder–decoder neural networks can be used to effectively formulate the sequence of the collision avoidance decision of the USV. Thus, this study significantly contributes to the increased efficiency and safety of maritime transportation. The proposed method can potentially be applied to the USV technology and intelligent collision-avoidance systems.

Highlights

With the increasing popularity of the automatic identification system (AIS) equipment and the development of shore-based and spaceborne AIS equipment, AIS data have become a popular data source for big data analysis and machine learning in the marine industry
Researchers can access and analyze the AIS data to improve the intelligence of maritime autonomous surface ships (MASSs)
This study proposes the usage of encoder–decoder neural networks by utilizing the sequence-to-sequence (Seq2Seq) model to learn the manner in which appropriate collision-avoidance decisions can be generated based on successful collision-avoidance cases

Summary

Introduction

With the increasing popularity of the automatic identification system (AIS) equipment and the development of shore-based and spaceborne AIS equipment, AIS data have become a popular data source for big data analysis and machine learning in the marine industry. Obtaining seamen-collision avoidance experience from the massive AIS data is important; the big-data-related technologies should urgently develop methodologies to satisfy their requirements. Intelligent collision avoidance technologies must be investigated to resolve navigation problems. Relevant experts and scholars have used various mechanisms, including expert systems [1], automatic collision avoidance systems [2], fuzzy theory [3], and dynamic route-planning systems [4]. They have studied the collision avoidance of ships at sea

Methods

Results

Conclusion