scholarly journals Evaluation of PNT Error Limits Using Real World Close Encounters from AIS Data

2021 ◽  
Vol 9 (2) ◽  
pp. 149
Author(s):  
Evelin Engler ◽  
Paweł Banyś ◽  
Hans-Georg Engler ◽  
Michael Baldauf ◽  
Frank Sill Torres
Keyword(s):  
Collision Avoidance ◽  
Real World ◽  
Simplified Model ◽  
Automatic Identification ◽  
Identification System ◽  
Traffic Data ◽  
Close Encounters ◽  
Data Provision ◽  
Erroneous Data ◽  
Better Than

Collision avoidance is one of the main tasks on board ships to ensure safety at sea. To comply with this requirement, the direct ship environment, which is often modelled as the ship’s domain, has to be kept free of other vessels and objects. This paper addresses the question to which extent inaccuracies in position (P), navigation (N), and timing (T) data impact the reliability of collision avoidance. Employing a simplified model of the ship domain, the determined error bounds are used to derive requirements for ship-side PNT data provision. For this purpose, vessel traffic data obtained in the western Baltic Sea based on the automatic identification system (AIS) is analysed to extract all close encounters between ships considered as real-world traffic situations with a potential risk of collision. This study assumes that in these situations, erroneous data can lead to an incorrect assessment of the situation with regard to existing collision risks. The size of the error determines whether collisions are detected, spatially incorrectly assigned, or not detected. Therefore, the non-recognition of collision risks ultimately determines the limits of tolerable errors in the PNT data. The results indicate that under certain conditions, the probability of non-recognition of existing collision risks can reach non-negligible values, e.g., more than 1%, even though position accuracies are better than 10 m.

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.

Ship Trajectories Pre-processing Based on AIS Data

2018 ◽  
Vol 71 (5) ◽  
pp. 1210-1230 ◽  
Author(s):  
Liangbin Zhao ◽  
Guoyou Shi ◽  
Jiaxuan Yang
Keyword(s):  
Data Mining ◽  
Traffic Density ◽  
Automatic Identification ◽  
Identification System ◽  
Traffic Data ◽  
Density Maps ◽  
Different Types ◽  
Quality Dimensions ◽  
Time Accuracy

Data derived from the Automatic Identification System (AIS) plays a key role in water traffic data mining. However, there are various errors regarding time and space. To improve availability, AIS data quality dimensions are presented for detecting errors of AIS tracks including physical integrity, spatial logical integrity and time accuracy. After systematic summary and analysis, algorithms for error pre-processing are proposed. Track comparison maps and traffic density maps for different types of ships are derived to verify applicability based on the AIS data from the Chinese Zhoushan Islands from January to February 2015. The results indicate that the algorithms can effectively improve the quality of AIS trajectories.

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 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.

The ‘ALVA CAPE’ and the Automatic Identification System: The Use of VHF in Collision Avoidance at Sea

2002 ◽  
Vol 55 (3) ◽  
pp. 431-442 ◽  
Author(s):  
S. J. Harding
Keyword(s):  
Case Studies ◽  
Collision Avoidance ◽  
Samuel Johnson ◽  
Automatic Identification ◽  
Identification System ◽  
Controversial Issues ◽  
Automatic Identification System ◽  
Radio Technology ◽  
Marine Navigation

One of the most controversial issues relating to marine navigation is the efficacy of ships' crews using VHF radio technology for bridge-to-bridge communications to agree manoeuvres. Through a re-evaluation of historic case studies, this paper provides background on the development of applying VHF technology in collision avoidance and the legislation, national and international, underpinning the practice; a practice that has found little or no support from the legal establishment. Finally the consequential development of a policy to require specific VHF technology to be installed on ships to facilitate agreements in relation to collision avoidance manoeuvres will be reviewed, that is the Automatic Identification System (AIS).Integrity without knowledge is weak and useless, and knowledge without integrity is dangerous and dreadful. Samuel Johnson

scholarly journals A Marine Autonomous Surface Craft for Long-Duration, Spatially Explicit, Multidisciplinary Water Column Sampling in Coastal and Estuarine Systems

2015 ◽  
Vol 32 (3) ◽  
pp. 627-641 ◽  
Author(s):  
Daniel L. Codiga
Keyword(s):  
Water Column ◽  
Collision Avoidance ◽  
Rhode Island ◽  
Coast Guard ◽  
Research Quality ◽  
Spatially Explicit ◽  
Automatic Identification ◽  
Identification System ◽  
Current Profile ◽  
Material Transport

AbstractThe Surveying Coastal Ocean Autonomous Profiler (SCOAP) is a large catamaran marine autonomous surface craft (MASC) for unattended weeks-long, spatially explicit, multidisciplinary oceanographic water column profile sampling in coastal/estuarine waterbodies. Material transport rates/pathways, crucial to understanding these ecosystems, are typically poorly known. SCOAP addresses demanding spatiotemporal sampling needs and operational challenges (strong currents, open coastal sea states, complex bathymetry, heavy vessel traffic). Its large size (11-m length, 5-m beam) provides seaworthiness/stability. The average speed of 2.5 m s−1 meets the representative goal to traverse an 18-km transect, sampling 10 min at each of 10 stations 2 km apart, nominally 4 times daily. Efficient hulls and a diesel–electric energy system can provide the needed endurance. The U.S. Coast Guard guidelines are followed: lighting, code flags, the Automatic Identification System (AIS), and collision avoidance regulations (COLREGs)-based collision avoidance (CA) by onboard autonomy software. Large energy reserves obviate low-power optimization of sensors, enabling truly multidisciplinary sampling, and provide on-demand propulsion for effective CA. Vessel stability facilitates high-quality current profile observations and will aid engineering/operation of the planned winched profiling system, performance of an anticipated radar system to detect/track non-AIS vessels, and potential research-quality meteorological sensor operation. A Narragansett Bay test deployment, attended by an escort vessel, met design goals; an unattended open coastal deployment is planned for Rhode Island Sound. Scientific and operational strengths of large catamaran MASCs suggest they could be an important cost-effective complement to other sampling platforms (e.g., improved spatiotemporal coverage and resolution, extending farther inshore, with a broader range of sensors, compared to underwater gliders) in coastal/estuarine waters.

scholarly journals Online Prediction of Ship Behavior with Automatic Identification System Sensor Data Using Bidirectional Long Short-Term Memory Recurrent Neural Network

Sensors ◽  
2018 ◽  
Vol 18 (12) ◽  
pp. 4211 ◽  
Author(s):  
Miao Gao ◽  
Guoyou Shi ◽  
Shuang Li
Keyword(s):  
Neural Network ◽  
Collision Avoidance ◽  
Recurrent Neural Network ◽  
Short Term Memory ◽  
Automatic Identification ◽  
Identification System ◽  
Short Term ◽  
Term Memory ◽  
Long Short Term Memory ◽  
Ship Behavior

The real-time prediction of ship behavior plays an important role in navigation and intelligent collision avoidance systems. This study developed an online real-time ship behavior prediction model by constructing a bidirectional long short-term memory recurrent neural network (BI-LSTM-RNN) that is suitable for automatic identification system (AIS) date and time sequential characteristics, and for online parameter adjustment. The bidirectional structure enhanced the relevance between historical and future data, thus improving the prediction accuracy. Through the “forget gate” of the long short-term memory (LSTM) unit, the common behavioral patterns were remembered and unique behaviors were forgotten, improving the universality of the model. The BI-LSTM-RNN was trained using 2015 AIS data from Tianjin Port waters. The results indicate that the BI-LSTM-RNN effectively predicted the navigational behaviors of ships. This study contributes significantly to the increased efficiency and safety of sea operations. The proposed method could potentially be applied as the predictive foundation for various intelligent systems, including intelligent collision avoidance, vessel route planning, operational efficiency estimation, and anomaly detection 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 Comprehensive Assessment of Automatic Identification System (AIS) Data Application to Anti-collision Manoeuvring

2015 ◽  
Vol 68 (4) ◽  
pp. 697-717 ◽  
Author(s):  
Andrzej Felski ◽  
Krzysztof Jaskólski ◽  
Paweł Banyś
Keyword(s):  
Collision Avoidance ◽  
Ship Motion ◽  
Supplementary Information ◽  
Automatic Identification ◽  
Identification System ◽  
Radio Communication ◽  
Dynamic Information ◽  
Automatic Identification System ◽  
Ideal System ◽  
Motion Parameters

The use of radar information for collision avoidance is common, however it is effective only for constant values of ship motion parameters. As information delays or information errors occur, it is reasonable to supplement the information derived from radar with another information system. An ideal system should operate automatically and continuously. A system that appears to be suitable to provide this kind of information is the Automatic Identification System (AIS), which may be classified as a radio communication system that uses radio waves to transmit data with regard to ship motion parameters. In this paper the topic of integrity and completeness of AIS information is discussed and the research results for the completeness and integrity of dynamic information are presented. In addition, the outcomes of AIS information correctness from the Gulf of Gdańsk were compared with studies carried out in the Baltic Sea, east of Bornholm, between Trelleborg and Arkona. The results of research for AIS dynamic information with the highest completeness (Position, Course over Ground and Speed over Ground) are presented. The research outcomes presented in the paper lead to the conclusion that AIS could deliver useful supplementary information in the process of collision avoidance.

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