Fuzzy Inference System for Determining Collision Risk of Ship in Madura Strait Using Automatic Identification System

Madura Strait is considered as one of the busiest shipping channels in Indonesia. High vessel traffic density in Madura Strait gives serious threat due to navigational safety in this area, i.e. ship collision. This study is necessary as an attempt to enhance the safety of marine traffic. Fuzzy inference system (FIS) is proposed to calculate risk collision of ships. Collision risk is evaluated based on ship domain, Distance to Closest Point of Approach (DCPA), and Time to Closest Point of Approach (TCPA). Data were collected by utilizing Automatic Identification System (AIS). This study considers several ships’ domain models to give the characteristic of marine traffic in the waterways. Each encounter in the ship domain is analyzed to obtain the level of collision risk. Risk level of ships, as the result in this study, can be used as guidance to avoid the accident, providing brief description about safety traffic in Madura Strait and improving the navigational safety in the area

Collision risk assessment based artificial potential field approach for multi-ships avoidance

1037-1047Artificial potential field (APF) is widely used for obstacles avoidance of autonomous surface vessel (ASV). However, its performance is poor for the case that the ASV encounters multiple ships. The collision may happen since the APF method is only taking distance and velocity into account. To solve this problem, a collision risk assessment based approach is proposed. A fuzzy logic system is applied to assess the collision risk with distance of close point of ppproaching (DCPA) and time of close point of approaching (TCPA). The collision risk is used to modify the APF for the ASV to avoid multiple ships. A critical encounter scenario is simulated to test the proposed approach. The simulation results indicate that the proposed method overcomes the problem to help an ASV successfully avoids the ship collision

Combined AI Capabilities for Enhancing Maritime Safety in a Common Information Sharing Environment

The complexity of maritime traffic operations indicates an unprecedented necessity for joint introduction and exploitation of artificial intelligence (AI) technologies, that take advantage of the vast amount of vessels’ data, offered by disparate surveillance systems to face challenges at sea. This paper reviews the recent Big Data and AI technology implementations for enhancing the maritime safety level in the common information sharing environment (CISE) of the maritime agencies, including vessel behavior and anomaly monitoring, and ship collision risk assessment. Specifically, the trajectory fusion implemented with InSyTo module for soft information fusion and management toolbox, and the Early Notification module for Vessel Collision are presented within EFFECTOR Project. The focus is to elaborate technical architecture features of these modules and combined AI capabilities for achieving the desired interoperability and complementarity between maritime systems, aiming to provide better decision support and proper information to be distributed among CISE maritime safety stakeholders

Novel Machine learning approach for Self-Aware prediction based on the Contextual reasoning

Machine learning is compelling in solving various applied problems. Nevertheless, machine learning methods lack the contextual reasoning capabilities and cannot be fitted to utilize additional information about circumstances, environments, backgrounds, etc. Such information provides essential knowledge about possible reasons for particular actions. This knowledge could not be processed directly by either machine learning methods. This paper presents the context-aware machine learning approach for actor behavior contextual reasoning analysis and context-based prediction for threat assessment. Moreover, the proposed approach uses context-aware prediction to tackle the interaction between actors. An idea of the technique lies in the cooperative use of two classification methods when one way predicts an actor’s behavior. The second method discloses such predicted action (behavior) that is non-typical or unusual. Such integration of two-method allows the actor to make the self-awareness threat assessment based on relations between different actors where some multidimensional numerical data define the connections. This approach predicts the possible further situation and makes its threat assessment without any waiting for future actions. The suggested approach is based on the Decision Tree and Support Vector Method algorithm. Due to the complexity of context, marine traffic data was chosen to demonstrate the proposed approach capability. This technique could deal with the end-to-end approach for safe vessel navigation in maritime traffic with considerable ship congestion

A novel marine radar targets extraction approach based on sequential images and Bayesian Network

This research proposes a Bayesian Network-based methodology to extract moving vessels from a plethora of blips captured in frame-by-frame radar images. First, the inter-frame differences or graph characteristics of blips, such as velocity, direction, and shape, are quantified and selected as nodes to construct a Directed Acyclic Graph (DAG), which is used for reasoning the probability of a blip being a moving vessel. Particularly, an unequal-distance discretisation method is proposed to reduce the intervals of a blip’s characteristics for avoiding the combinatorial explosion problem. Then, the undetermined DAG structure and parameters are learned from manually verified data samples. Finally, based on the probabilities reasoned by the DAG, judgments on blips being moving vessels are determined by an appropriate threshold on a Receiver Operating Characteristic (ROC) curve. The unique strength of the proposed methodology includes laying the foundation of targets extraction on original radar images and verified records without making any unrealistic assumptions on objects' states. A real case study has been conducted to validate the effectiveness and accuracy of the proposed methodology

Telematic Support in Improving Safety of Maritime Transport

Security mechanisms of a telematics system are exceedingly intersecting as they could pretend the ordinary influence of the vehicle and perhaps terminate in accidents. This paper includes a new look at automotive and telematics transportation systems, also refers to methods in modelling, facility location, data processing and assessment of risk in telematics networks

A Study on Development of Expert System for Collision Avoidance and Navigation Based on AIS

오늘날 무역량의 급속한 증가로 세계 주요 항로에서의 해상 교통량은 폭주하고 있다. 더욱이 선박은 대형화와 함께 고속화 되고 있으며 또한 전용화가 이루어 지고 있다. 이런 환경으로 해상에서의 선박 충돌 사고 계속 발생하고 있어 이런 충돌로 인하여 인명 및 재산에 큰 손해를 발생할 뿐만 아니라 심각한 해상 오염을 발생하기도 한다. 한편, 높은 수준의 경제 성장에 따라 사람들은 승선 근무를 기피하게 되어 항해자의 직무 능력은 과거에 비하여 떨어져 있는 편이다. 그런데도 불구하고 항해 중의 의사 결정은 전적으로 책임 항해사의 경험과 지식에 의존하고 있다. 항해사 혹은 선장이 취한 의사 결정은 자신의 선박과 주위의 선박의 운명을 결정하게 된다. 그러나 경험이 많은 선장 및 항해사의 수는 선박 척수보다는 훨씬 적다. 신규의 항해사들은 짧은 시간에 그런 값진 경험들을 습득할 수가 없다. 이런 경험을 적절하게 이용하여 해상에서의 충돌을 효과적으로 줄이기 위하여 이 논문에서는 충돌 회피 및 항해 전문가 시스템(expert system for collision avoidance and navigation, ESCAN)을 제안한다. 신규 항해사들의 낮은 능력을 보완하기 위한 하나의 방법으로 ESCAN 은 충돌 회피에 관한 합리적인 권고를 항해사들에게 제시하여 현재의 교통 상황을 더 이해하게 하고 충돌의 위험이 발생할 때 충돌 회피에 관한 합리적 의사 결정을 하게 한다. 레이더/ARPA 와 같은 장비는 충돌 회피에 관한 단순한 기능을 제공하여 이들 장비에서 나타난 정보는 짧은 시간에 충돌 회피의 의사 결정을 하는데 효과적이지 못하여 충돌 회피에 관한 정보 및 지시 등이 더 필요하게 한다. 한편 AIS 기술 활용하여 이 논문에서 개발한 ESCAN 은 본선 주위에 있는 상대 선박에 관한 보다 유용한 항해 정보를 받을 수 있어 현재의 상황을 처리하는데 보다 나은 권고나 제안을 할 수 있다. 이 논문에서 얻은 결론은 다음과 같다. 먼저 해상충돌방지규칙(COLREGS)와 충돌회피과정, 그와 관련된 내용을 검토하였으며 그 내용은 다음과 같다. (1) 해상에서의 충돌을 예방하고 선박의 안전 항해를 확보하기 위해서 COLREGS 를 정확하게 이해하고 엄격하게 따라야 한다. (2) 안전 속력은 효과적인 충돌 회피 동작을 결정하고 취하는데 충분한 시간을 확보하는 1 차적인 요소이다. 항해 중 그 상황에 맞는 속력을 적절하게 유지하여야 한다. (3) 항해 중 안전한 통과 거리를 확보하여야 하는데 대양 항해에서는 통상 2 마일로 간주한다. (4) 양 선박이 조우할 때 과정은 충돌 회피 동작의 효과가 없는 단계, 충돌의 위험성이 있는 단계, 극한 상황에 있는 단계, 충돌 위험(거의 충돌하는) 단계 등으로 나눌 수 있다. (5) 통상 항해사들은 충돌의 위험이 제일 큰 선박을 결정하는데 충돌위험도 값을 사용한다. ESCAN 에서는 공식 (2-2)를 이용하여 충돌위험도를 평가한다. (6) 본선이 여러 선박과 조우할 때 ESCAN 은 본선과 상대 선박과의 조우 상황을 분석하여 각 선박의 가능한 움직임을 예측한다. 또 어떤 선박을 제일 먼저 피할 것인지 정하고 각각의 선박에 대하여 안전한 충돌 회피 동작 및 시간을 결정한다. 한편 항해사는 현재 상황에 대한 안전 통과 거리를 고려하여 ESCAN 에서 제공한 안전 충돌 회피 영역(방위, 속력)이 적절한지를 확인한다. 이런 방법을 이용하여 현재의 다수의 선박의 조우 상황에 대하여 적절한 의사 결정을 할 수 있다. 두 번째로 ESCAN 을 설계하고 개발하였는데 그 결과는 다음과 같다. (10) ESCAN 은 전문가 시스템의 이론과 기술을 이용하여 설계하고 개발하였으며 AIS, 레이더/ARPA 정보를 기반으로 하였다. (11) ESCAN 은 항해 장비의 데이터를 보존하는 데이터베이스(Facts/Data Base), ESCAN 의 프로덕션 룰을 저장하는 지식베이스(Knowledge Base), 데이터에 알맞은 규칙을 결정하는 추론기구(Inference Engine), 사용자와 ESCAN 과의 통신을 위한 사용자-시스템 인터페이스(User-System Interface) 등으로 4 가지로 구성되어 있다. (12) ESCAN에서는 AIS 기술을 사용한다. AIS는 본선이 본선 주위에 있는 상대 선박에 관한 상세한 항해 정보를 제공한다. 그러므로 이를 이용하면 의사 결정을 보다 합리적으로 할 수 있다 (13) ESCAN 에 사용된 항해 지식은 COLREGS 및 항해 전문가의 지식을 기반으로 한 것이다. (14) ESCAN 의 지식 베이스는 모듈 구조로 되어 있으며 그 내용은 기본 항해 규칙 모듈, 조종 평가 모듈, 조우 단계 구별 모듈, 조우 상태 판단 모듈, 추가 충돌 회피 지식 모듈, 항해 경험 및 다수의 선박의 회피 모듈 등의 6 개의 모듈이다. (15) 프로덕션 룰을 ESCAN 에서 충돌 회피에 관한 지식을 표현하는데 사용하였다. 그 이유는 프로덕션 룰의 구조가 이런 지식을 완전하게 표현할 수 있고 또 CLIPS 언어로 잘 지원될 수 있기 때문이다. (16) ESCAN 에 사용된 충돌 회피에 관한 새로운 추론 과정은 그림 3-8 과 같다. (17) ESCAN 은 전향추론과 후향추론을 혼합한 형태를 사용하였다. (18) CLIPS 는 레터 패턴 매칭 알고리즘을 사용하므로 ESCAN 의 반응 속도는 상당히 향상되었다. 마지막으로 ESCAN 을 실험하여 다음과 같은 결과를 얻었다. (1) ESCAN 의 추론 부분은 CLIPS 로 프로그램 되어 있지만 나머지 부분은 비쥬얼 C++로 되어 있다. (2) ESCAN 은 본선과 상대 선박이 조우하는 여러 가지 상황에 대하여 실시간으로 분석하고 판단하는 기능을 가지고 있으며 항해사들에게 적절한 충돌 회피 계획, 충고, 혹은 권고 등을 제공한다. (3) 또 ESCAN 은 사용자가 충돌 회피 동작을 시뮬레이션 할 수 있는 기능을 가지고 있다. (4) 이 연구에서 제시한 몇 가지 예를 따르면 ESCAN 은 COLREGS 규칙을 따르고 있으며 아울러 항해 전문가의 조언을 따르고 있다. (5) 장차 규칙을 추가하고자 할 때 추가 업그레이드가 가능하다. 이것은 전 시스템을 고치는 것이 아니라 지식 베이스에 사용된 규칙만을 다시 쓰면 되기 때문이다. (6) 다수 선박의 조우 상황에서는 모든 조건을 만족하는 현재의 상황을 처리할 수 있는 표준 케이스를 제공하고 있다. ESCAN 의 개발은 항해사가 합리적인 판단을 하는데 도움을 주어 안전항해를 하게 할 뿐만 아니라 항해 장비를 통합하여 무인 항해가 가능한 통합자동항법시스템의 개발까지 연계될 수 있다. 앞으로 다른 항법시스템과 통하여 사용자에게 편리한 시스템을 구성하는 연구가 남아 있으며, 또 실선에서의 실험을 통하여 ESCAN 을 보완하는 연구가 남아 있다.Nowadays, highly increasing global trade has caused heavy traffic in the main sea routes. Moreover, ships are getting larger and larger in size, faster in speed and highly specialized. Under these circumstances, serious collision accidents between ships happened at sea over and over again, and led to not only huge loss of life and property but also serious damage to marine environment. Meanwhile, due to the high level of economic growth, more and more people tend to choose their jobs in land rather than them aboard ships. Therefore, their competence as navigation officers becomes worse now than in the past. Even so decision-making during navigation entirely depends on the experience and knowledge of responsible officers or shipmasters aboard. During navigation, decision-making made by them can determine the fate of own ship and the ships in the vicinity of her. However, the number of experienced navigation officers or shipmasters is far less than that of the world fleet. New seafarers can not absorb and comprehend such precious experience in a short time. In order to adequately utilize the experience and effectively reduce collisions at sea, an expert system for collision avoidance and navigation (hereinafter called ？gESCAN？h) is proposed in this paper. As a method to come up with the low competence of new seafarers, the ESCAN can provide them with reasonable recommendations of collision avoidance or can help them to know better about current traffic situation and make more reasonable decisions of collision avoidance when dangerous situations happen. Some equipment like radar/ARPA can provide a very simple function for collision avoidance. However, the information obtained from such equipment can not effectively help new seafarers to make reasonable decision-making of collision avoidance in a short time, and they need more helpful information and instructions of collision avoidance. On the other hand, with use of AIS technology, the ESCAN developed in this paper can receive more useful navigational information of other ships in the vicinity of own ship and can provide more sophisticated recommendations or suggestions for dealing with current situation. The following are conclusions from this study. Firstly, COLREGS, the process of collision avoidance and some other related aspects are discussed here. Some results are given as follows: (1) In order to prevent and avoid collisions at sea, and to secure safe navigation of ships, COLREGS needs to be correctly comprehended and strictly carried out. (2) Safe speed is a primary factor ensuring if own ship has enough time to determine and take proper and effective avoidance actions. During navigation, it should be appropriately determined so as to adapt to prevailing circumstances and conditions. (3) Safe passing distance should be maintained during navigation. Normally in open sea two(2) nautical miles are considered to be sufficient. (4) Encountering process of two ships can be divided into 4 phases such as phase of effect-free action, phase of involving risk of collision, phase of involving close-quarters situation and phase of involving danger of collision. (5) Usually, navigators use value of collision risk to know the risk of collision and to select the primary target to avoid. In ESCAN, formula (2-2) is used to appraise the value of collision risk. (6) If own ship is involved in a multi-target encountering situation, ESCAN will analyze the encountering situations between own ship and other ships, predict possible movement of other ships, determine which target is the primary one to avoid, and determine avoiding action and the time to take. Meanwhile, navigators should also consider the safe passing distance of current situation and the safe zone of collision avoidance provided by ESCAN. By using this approach, appropriate decision-making for dealing with current multi-target encountering situation of can be acquired. Secondly, detailed design of ESCAN is introduced and some results can be drawn as follows: (1) The ESCAN is designed and developed by using the theory and technology of expert system and based on information provided by AIS and radar/ARPA system. (2) It is composed of four components. Facts/Data Base in charge of preserving data from navigational equipment, Knowledge Base storing production rules of the ESCAN, Inference Engine deciding which rules are satisfied by facts, User-System Interface for communication between users and ESCAN. (3) In ESCAN, AIS technology is used. AIS can help own ship to receive more detailed navigational information from the ships in the vicinity of her. Therefore, more reasonable decision-making can be determined according to such abundant information. (4) Navigational knowledge used in ESCAN is based on COLREGS and other navigation expertise. (5) Module structure is used to build the knowledge base of ESCAN. And it is divided into six modules such as basic navigational rules module, maneuverability judgment module, division of encountering phase module, encountering situation judgment module, auxiliary knowledge of collision avoidance module, and navigation experience and multi-ship encountering scene avoiding action module. (6) Production rules are used to represent the knowledge of collision avoidance in ESCAN because the structure of them is perfect for representing such knowledge and they are supported by CLIPS well. (7) A new inference process of collision avoidance as shown in Fig.3-8 is used in ESCAN. (8) Mixed inference which combines forward inference and backward inference is used in ESCAN. (9) Because CLIPS adopts Rete Pattern-Matching Algorithm, response speed of ESCAN is greatly increased. Finally, detailed implementation of ESCAN is introduced and some conclusions are given as follows: (1) The part of ESCAN in charge of inference is programmed in CLIPS and the remaining part of it is programmed in Visual C++. (2) The ESCAN has the function of real-time analysis and judgment of various encountering situations between own ship and targets, and is to provide navigators with appropriate plans of collision avoidance and additional advice and recommendation. (3) Auxiliary functions of ESCAN are convenient for users such as simulation function which can simulate avoiding actions provided by ESCAN. (4) According to the results of the examples, the suggestions provided by ESCAN conform to the rules of COLREGS and the advice given by navigation experts well. (5) It is easy to upgrade ESCAN when rules are required to be upgraded in the future. Only rules in Knowledge Base should be rewritten rather than the whole system. (6) Multi-target encountering case matching function of ESCAN can provide a recorded reference case for dealing with current situation if all the conditions of the case are matched. Development of ESCAN not only can help navigators make more reasonable decision-making of collision avoidance so as to ensure safe navigation of ships, but also can promote the development of integrated automatic navigation system which integrates all shipborne systems and implements intelligent unmanned navigation. The future study will deal with integrating ESCAN with other shipborne systems and make it more user-friendly and will carry out the experiment on board which is the important part of ESCAN.Chapter 1 Introduction = 1 1.1 Background and Purpose of the Study = 1 1.2 Introduction of AIS = 5 1.3 Introduction of Expert System and CLIPS = 8 1.4 Related Studies of the Study = 9 1.4.1 Related Studies in China = 9 1.4.2 Related Studies in Other Countries = 10 1.4.3 Principal Research Method in the Related Studies = 11 1.5 Scope and Content of the Study = 13 Chapter 2 Analysis and Research of COLREGS and Collision Avoidance = 14 2.1 COLREGS = 14 2.1.1 Introduction of COLREGS = 14 2.1.2 Content of COLREGS = 15 2.1.3 Look-out = 15 2.1.4 Safe Speed = 16 2.1.5 Risk of Collision = 17 2.1.6 Criterions for Appraising Avoiding Actions = 18 2.2 Process of Collision Avoidance = 19 2.2.1 Flow Chart of Collision Avoidance = 19 2.2.2 Safe Passing Distance = 23 2.2.3 Division of Encountering Process = 23 2.2.4 Division of Encountering Situations of Ships in Sight of One = 27 2.2.5 Avoiding Actions of Ships not in Sight of One Another Because... = 29 2.2.6 Division of Avoiding Actions = 34 2.3 Value of Collision Risk = 35 2.3.1 Approaches for Appraising Collision Risk = 35 2.3.2 Approaches Using Specialities of Sech Function for Appraising... = 42 2.4 Multi-target Collision Avoidance = 54 2.4.1 Judging Encountering Situations with Target-ships = 55 2.4.2 Predicting the Movement Trends of Target-ships = 57 2.4.3 Determining the Primary Target-ship to Avoid = 58 2.4.4 Determining Timing of Taking Avoiding Actions = 60 2.4.5 Considering the Safe Action Zones = 62 2.4.6 Considering the Typical Cases of Multi-ship Collision ... = 63 2.4.7 Approach for Dealing with Multi-target Situation in ESCAN = 66 Chapter 3 Design of ESCAN = 67 3.1 Design of Integrated Structure = 67 3.1.1 External Connection of ESCAN = 67 3.1.2 Structure of ESCAN 6 = 9 3.2 Design of Facts/Data Base = 70 3.3 Design of Knowledge Base = 74 3.3.1 Sources of the Knowledge of Collision Avoidance = 74 3.3.2 Process of Building Knowledge Base = 75 3.3.3 Module Structure of Knowledge Base = 77 3.3.4 Knowledge Representation = 82 3.3.5 Management of Knowledge Base = 107 3.4 Design of Inference Engine = 108 3.4.1 Introduction of Inference Engine = 108 3.4.2 Inference Process of ESCAN = 110 3.4.3 Approaches of Deduction Inference = 114 3.4.4 Pattern-Matching Algorithm = 116 3.4.5 Conflict Resolution = 119 3.5 Design of User-System Interface = 120 Chapter 4 Implementation of ESCAN = 121 4.1 Principles for Developing Expert Systems = 121 4.2 Functional Description of ESCAN = 123 4.3 Computing Formulas Used in ESCAN = 124 4.3.1 Formulas for Calculating Information of Relationship ... = 125 4.3.2 Formulas for Calculating Information of Relationship ... = 127 4.3.3 Formulas for Calculating Position of One Target-ship by ... = 129 4.4 Approach for Judging Whether Ships Have Kept Well Clear off ... = 130 4.5 Approach for Determining Magnitude of Avoiding Action = 131 4.6 Software for Developing ESCAN = 132 4.6.1 Two Types of Software = 132 4.6.2 Embedding CLIPS in Visual C++ = 132 4.7 Building the Modules of Knowledge Base = 133 4.8 Layout of User-System Interface = 134 4.8.1 Main User-System Interface = 134 4.8.2 Other Interfaces = 137 4.9 Practical Functions of ESCAN = 137 4.9.1 Primary Function = 137 4.9.2 Auxiliary Functions = 139 4.10 Using ESCAN to Deal with Single Target-ship Encountering ... = 144 4.10.1 Head-on Situation = 144 4.10.2 Overtaking Situation = 146 4.10.3 Crossing Situation = 147 4.11 Using ESCAN to Deal with Multiple Target-ships Encountering ... = 147 4.11.1 Determining Encountering Situation with Each Target-ship = 148 4.11.2 Selecting the Primary Target-ship to Avoid = 149 4.11.3 Determining Avoiding Action and Timing to Take = 150 4.11.4 Determining Safe Action Zone = 151 4.11.5 Simulating the Determined Avoiding Action = 152 4.11.6 Multi-target Encountering Case Matching = 155 Chapter 5 Conclusion = 159 References = 164 Annex I Content of COLREGS = 171 List of Published Papers during Doctoral Course = 173 Acknowledgements = 17

Early detection of vessel collision situations in a vessel traffic services area

This study presents enhanced collision detection model in a Vessel Traffic Services (VTS) area. The proposed detection method of collision situations is based on the assumption that VTS station is provided with passage plans of all the vessels in a monitored area. By using an early detection model for prediction of possible collision situations, VTS stations could switch from the area-monitoring concept to the passage-monitoring system. An early detection model of collision risks in a VTS area uses vessels’ dynamic characteristics as inputs (vessels’ position, course over ground and speed), and delivers prediction of their future positions as output. In order to achieve the desired accuracy, the model takes into the account the intended course alterations and the impending environmental loads. The model is able to provide the outputs as early as the passage plans are submitted to a VTS monitored area. Hence, when discussing model’s capability for early detection of collision situations, improved VTS operating standards could be developed in order to achieve safer passages through enhanced collision avoidance strategies. Simulation results clearly show the advantages of the proposed model as a decision support tool for a VTS operator when combining passage plans with the analysis of environmental loads. First published online 18 November 201