scholarly journals A Novel Method for Risk Assessment and Simulation of Collision Avoidance for Vessels based on AIS

Algorithms ◽  
2018 ◽  
Vol 11 (12) ◽  
pp. 204 ◽  
Author(s):  
ManhCuong Nguyen ◽  
Shufang Zhang ◽  
Xiaoye Wang
Keyword(s):  
Mathematical Model ◽  
Risk Assessment ◽  
Collision Avoidance ◽  
Warning System ◽  
Automatic Identification ◽  
Identification System ◽  
Novel Method ◽  
Ship Collision ◽  
Collision Avoidance System ◽  
Traffic Detection

The identification of risks associated with collision for vessels is an important element in maritime safety and management. A vessel collision avoidance system is a topic that has been deeply studied, and it is a specialization in navigation technology. The automatic identification system (AIS) has been used to support navigation, route estimation, collision prediction, and abnormal traffic detection. This article examined the main elements of ship collision, developed a mathematical model for the risk assessment, and simulated a collision assessment based on AIS information, thereby providing meaningful recommendations for crew training and a warning system, in conjunction with the AIS on board.

scholarly journals Navigation Situation Clustering Model of Human-Operated Ships for Maritime Autonomous Surface Ship Collision Avoidance Tests

2021 ◽  
Vol 9 (12) ◽  
pp. 1458
Author(s):  
Taewoong Hwang ◽  
Ik-Hyun Youn
Keyword(s):  
Collision Avoidance ◽  
Automatic Identification ◽  
Identification System ◽  
The West ◽  
Test Environment ◽  
Clustering Model ◽  
Data Driven Approach ◽  
Ship Collision ◽  
Collision Avoidance System ◽  
Autonomous Ship

The collision avoidance system is one of the core systems of MASS (Maritime Autonomous Surface Ships). The collision avoidance system was validated using scenario-based experiments. However, the scenarios for the validation were designed based on COLREG (International Regulations for Preventing Collisions at Sea) or are arbitrary. Therefore, the purpose of this study is to identify and systematize objective navigation situation scenarios for the validation of autonomous ship collision avoidance algorithms. A data-driven approach was applied to collect 12-month Automatic Identification System data in the west sea of Korea, to extract the ship’s trajectory, and to hierarchically cluster the data according to navigation situations. Consequently, we obtained the hierarchy of navigation situations and the frequency of each navigation situation for ships that sailed the west coast of Korea during one year. The results are expected to be applied to develop a collision avoidance test environment for MASS.

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

2020 ◽  
Vol 8 (9) ◽  
pp. 682
Author(s):  
Jia-hui Shi ◽  
Zheng-jiang Liu
Keyword(s):  
Big Data ◽  
Collision Avoidance ◽  
Difficult Problem ◽  
Automatic Identification ◽  
Identification System ◽  
Unmanned Surface Vehicle ◽  
Effective Learning ◽  
Maritime Navigation ◽  
Ship Collision ◽  
Gated Recurrent Unit

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.

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

2020 ◽  
Vol 8 (10) ◽  
pp. 754
Author(s):  
Miao Gao ◽  
Guo-You Shi
Keyword(s):  
Neural Networks ◽  
Decision Making ◽  
Time Series ◽  
Collision Avoidance ◽  
Time Series Data ◽  
Series Data ◽  
Automatic Identification ◽  
Identification System ◽  
Automatic Response ◽  
Ship Collision

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.

scholarly journals AIS Politics: The Contested Use of Vessel Tracking at the EU’s Maritime Frontier

2019 ◽  
Vol 44 (5) ◽  
pp. 881-899 ◽  
Author(s):  
Lorenzo Pezzani ◽  
Charles Heller
Keyword(s):  
Mediterranean Sea ◽  
Collision Avoidance ◽  
Tracking System ◽  
Automatic Identification ◽  
Identification System ◽  
Automatic Identification System ◽  
Vessel Tracking ◽  
Collision Avoidance System ◽  
The Mediterranean ◽  
Do So

Automatic identification system (AIS) is a vessel tracking system, which since 2004 has become a global tool for the detection and analysis of seagoing traffic. In this article, we look at how this technology, initially designed as a collision avoidance system, has recently become involved in debates concerning migration across the Mediterranean Sea. In particular, after having briefly discussed its emergence and characteristics, we examine how through different practices of (re)appropriation AIS, and the data it generate, have been seized upon, both to contest and to sustain the exclusionary nature of borders, and the mass dying of migrants at sea to which it leads. We do so by referring to forms of data activism we have contributed to in the frame of our Forensic Oceanography project as well as to situations in which AIS has been mobilized by xenophobic groups to demand even stronger exclusionary measures. At the same time, we point to the multiplicity of actors who participate in the politics of migration through AIS in unexpected ways. We conclude by highlighting the irreducible ambivalence of practices of appropriation and call for persistent attention to one’s own positioning within the global datascape constituted by AIS and other data.

scholarly journals Improving Near Miss Detection in Maritime Traffic in the Northern Baltic Sea from AIS Data

2021 ◽  
Vol 9 (2) ◽  
pp. 180
Author(s):  
Lei Du ◽  
Osiris A. Valdez Banda ◽  
Floris Goerlandt ◽  
Pentti Kujala ◽  
Weibin Zhang
Keyword(s):  
Baltic Sea ◽  
Perceived Risk ◽  
Maritime Transportation ◽  
Near Miss ◽  
Automatic Identification ◽  
Identification System ◽  
Collision Risk ◽  
Ship Collision ◽  
The Impact ◽  
Ship Behavior

Ship collision is the most common type of accident in the Northern Baltic Sea, posing a risk to the safety of maritime transportation. Near miss detection from automatic identification system (AIS) data provides insight into maritime transportation safety. Collision risk always triggers a ship to maneuver for safe passing. Some frenetic rudder actions occur at the last moment before ship collision. However, the relationship between ship behavior and collision risk is not fully clarified. Therefore, this work proposes a novel method to improve near miss detection by analyzing ship behavior characteristic during the encounter process. The impact from the ship attributes (including ship size, type, and maneuverability), perceived risk of a navigator, traffic complexity, and traffic rule are considered to obtain insights into the ship behavior. The risk severity of the detected near miss is further quantified into four levels. This proposed method is then applied to traffic data from the Northern Baltic Sea. The promising results of near miss detection and the model validity test suggest that this work contributes to the development of preventive measures in maritime management to enhance to navigational safety, such as setting a precautionary area in the hotspot areas. Several advantages and limitations of the presented method for near miss detection are discussed.

An Automatic Collision Avoidance System Based on LiDAR

2014 ◽  
Vol 644-650 ◽  
pp. 952-956 ◽  
Author(s):  
Hao Yang ◽  
An Qing You ◽  
Wen Wu Pan ◽  
Hai Long Tang
Keyword(s):  
Mathematical Model ◽  
Collision Avoidance ◽  
Point Cloud ◽  
Gps Data ◽  
3D Point Cloud ◽  
The Road ◽  
3D Engine ◽  
Collision Avoidance System

For vehicle-borne LiDAR, a mathematical model is built for the computation and reconstruction of laser point cloud with the scanning data, GPS data and IMU data. 3D point cloud of the road and the scenery on the both sides of the road is obtained. Then according to the trajectory of the vehicle, 3D roaming for the scenery on the both sides of the road is realized using OpenGL 3D engine technology. This technology provides a probably feasible way for anti-collision of vehicles and aircrafts when driven at night, in the heavy fog or flying between the mountains.

scholarly journals An unmanned aircraft system for maritime operations

2018 ◽  
Vol 15 (4) ◽  
pp. 172988141878633 ◽  
Author(s):  
Mario Monteiro Marques ◽  
Victor Lobo ◽  
R Batista ◽  
J Oliveira ◽  
A Pedro Aguiar ◽  
...  
Keyword(s):  
Collision Avoidance ◽  
Situational Awareness ◽  
Traffic Monitoring ◽  
Unmanned Aircraft ◽  
Automatic Identification ◽  
Identification System ◽  
Time Operation ◽  
Air System ◽  
Unmanned Air System ◽  
Aircraft System

Unmanned air systems are becoming ever more important in modern societies but raise a number of unresolved problems. There are legal issues with the operation of these vehicles in nonsegregated airspace, and a pressing requirement to solve these issues is the development and testing of reliable and safe mechanisms to avoid collision in flight. In this article, we describe a sense and avoid subsystem developed for a maritime patrol unmanned air system. The article starts with a description of the unmanned air system, that was developed specifically for maritime patrol operations, and proceeds with a discussion of possible ways to guarantee that the unmanned air system does not collide with other flying objects. In the system developed, the position of the unmanned air system is obtained by the global positioning system and that of other flying objects is reported via a data link with a ground control station. This assumes that the detection of those flying objects is done by a radar in the ground or by self-reporting via a traffic monitoring system (such as automatic identification system). The algorithm developed is based on game theory. The approach is to handle both the procedures, threat detection phase and collision avoidance maneuver, in a unified fashion, where the optimal command for each possible relative attitude of the obstacle is computed off-line, therefore requiring low processing power for real-time operation. This work was done under the research project named SEAGULL that aims to improve maritime situational awareness using fleets of unmanned air system, where collision avoidance becomes a major concern.

scholarly journals Research Design of Mobile Based Decision Support for Early Flood Warning System

2020 ◽  
Vol 14 (17) ◽  
pp. 130
Author(s):  
Mohd Faizal Omar ◽  
Mohd Nasrun Mohd Nawi ◽  
Jastini Mohd Jamil ◽  
Ani Munirah Mohamad ◽  
Saslina Kamaruddin
Keyword(s):  
Mathematical Model ◽  
Risk Assessment ◽  
Decision Making ◽  
Decision Support ◽  
Research Design ◽  
Flood Risk ◽  
Architectural Design ◽  
Warning System ◽  
Economic Losses ◽  
Flood Risk Assessment

Flooding has become one of the most rapidly growing types of natural disaster that has spread around the globe. It is is one of the major natural hazards in many countries and mostly affected in the low-lying or flood prone areas. In order to minimize loss of life and economic losses, a detailed and comprehensive decision making tool is necessary for both flood control planning and emergency service operations. In this paper, we demonstrate our research design for mobile based decision support of Flood Early Warning System (FEWS). We outlined four research objectives. Firstly, critical criteria for flood risk assessment will be identified and the second step will involve develop measurement model for relative flood risk using Geographic Information System (GIS), Multi Attribute Decision Making (MADM) and data mining technique. In the third objectives, the holistic architectural design is develop by incorporating the communication technology and other related ICT requirements for the mobile decision support. The fourth objective is to validate the mathematical model and architectural design. Case study approach is chosen in order to understand the flood event and validate the decision support model. Following well-defined procedures, flood maps were drawn based on the data collected from expert responses to a questionnaire, the field survey, satellite images, and documents from flood management agencies. It is anticipates that by integrating of mathematical model, GIS and mobile application in flood risk assessment could provide useful detailed information for flood risk management, evacuation, communication. The decision support design from this study is perhaps to improve the warning system and contribute to reduction of casualties.

scholarly journals Information unfitness as a factor constraining Automatic Identification System (AIS) application to anti-collision manoeuvring

2012 ◽  
Vol 19 (3) ◽  
pp. 60-64 ◽  
Author(s):  
Andrzej Felski ◽  
Krzysztof Jaskólski
Keyword(s):  
Decision Making ◽  
Collision Avoidance ◽  
Significant Role ◽  
Technical Specification ◽  
Supplementary Information ◽  
Automatic Identification ◽  
Identification System ◽  
Radar Measurement ◽  
Dynamic Data ◽  
Automatic Identification System

ABSTRACT Common use of shipboard AIS creates conditions for the use of a new kind of dynamic data in the situation of the risk of collision. AIS position report is a source of supplementary information derived from error leveraged radar measurement. However, in view of the results of the studies there are opinions with regard to inconsistent AIS dynamic data in the process of decision-making by the officer of the watch. By taking into consideration the recordings of the studies and technical specification of AIS it can be concluded that the results of inconsistent data have significant role in collision avoidance manoeuvring.

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