Inference processes in the automatic communication system for autonomous vessels

The era of autonomous ships has already begun in maritime transport. The 30-year forecast for the development of marine technologies predicts many autonomous vessels at sea. This will necessitate radical implementation of new intelligent maritime navigation systems. One of the intelligent systems that has to be implemented is a collision avoidance system. The inference process is a key element of autonomous manoeuvres. These authors propose an inference process that enables exchange of information, intentions and expectations between autonomous vessels and gives them an opportunity to negotiate a safe manoeuvre satisfying all the parties concerned. The model of inference in the communication process has been presented. Methods and algorithms for information exchange and negotiation have been developed. These models were implemented and tested under various conditions. The results of case studies indicate that it is possible to effectively communicate and negotiate used the developed method. To demonstrate the effectiveness of the presented approach over 30 random simulations have been carried out. After successful laboratory tests, over 100 scenarios were executed in quasi-real conditions and fully operational conditions. Tests were carried out in the center of the Foundation for the Safety of Navigation and Environmental Protection on Lake Silm in Iława, Poland. In the framework of project AVAL (Autonomous Vessel with an Air Look) POIR.04.01.04-00-0025-16,  82 random scenarios involving four vessels were performed and 60 random scenarios with two vessels. In 2020 tests were carried out in real conditions on the ferries Wolin and m/f Gryf. The communication and negotiation system presented in the article has been designed and developed specially for maritime navigation purposes. The authors believe that the presented solution can be one of various solutions implemented in autonomous shipping in the near future

A semi-supervised deep learning model for ship encounter situation classification

Maritime safety is an important issue for global shipping industries. Currently, most of collision accidents at sea are caused by the misjudgement of the ship’s operators. The deployment of maritime autonomous surface ships (MASS) can greatly reduce ships’ reliance on human operators by using an automated intelligent collision avoidance system to replace human decision-making. To successfully develop such a system, the capability of autonomously identifying other ships and evaluating their associated encountering situation is of paramount importance. In this paper, we aim to identify ships’ encounter situation modes using deep learning methods based upon the Automatic Identification System (AIS) data. First, a segmentation process is developed to divide each ship’s AIS data into different segments that contain only one encounter situation mode. This is different to the majority of studies that have proposed encounter situation mode classification using hand-crafted features, which may not reflect the actual ship’s movement states. Furthermore, a number of present classification tasks are conducted using substantial labelled AIS data followed by a supervised training paradigm, which is not applicable to our dataset as it contains a large number of unlabelled AIS data. Therefore, a method called Semi-Supervised Convolutional Encoder–Decoder Network (SCEDN) for ship encounter situation classification based on AIS data is proposed. The structure of the network is not only able to automatically extract features from AIS segments but also share training parameters for the unlabelled data. The SCEDN uses an encoder–decoder convolutional structure with four channels for each segment (distance, speed, Time to the Closed Point of Approach (TCPA) and Distance to the Closed Point of Approach (DCPA)) been developed. The performance of the SCEDN model are evaluated by comparing to several baselines with the experimental results demonstrating a higher accuracy can be achieved by our proposed model

Studying Control Processes for Bridge Teams

Several technological advances have been seen the maritime domain to achieve higher operational efficiency and to address the generally recognised causes of most maritime accidents. The International Maritime Organization (IMO) endorses the use of best available technology to “drive continuous improvement and innovation in the facilitation of maritime traffic” in line with the goal of sustainable development. It is commonly acknowledged that modern technology revolutionized marine navigation, and presently it has a large potential to increase safety in navigation. However, the incorporation of new technologies in support of navigation also brought unforeseen critical consequences, contributing to unsafe practices, or even to accidents or incidents. Several issues were associated with human factors. To properly address the adoption of the newest technology in support of safe navigation, IMO established the e-navigation concept, currently under implementation. The complexity of the maritime socio-technical system requires novel theoretical foundations, since many of the present framework rely on the analysis of accidents. The design of complex maritime navigation system must take place on several levels, providing different perspectives over the system problems. The evaluation and design of technologies envisaged by the e-navigation concept requires a better understand of how teams perform the navigation work in the pursuit of safe navigation. This study attempts to provide a better understanding on how maritime navigation is currently done on-board, considering the overarching elements and their interactions. In maritime navigation safety is a transverse issue, and that is why we need to know the conditions for safe navigation to improve the design of ship navigation control. The work supporting this thesis was focused on: (i) understanding how navigation is done and to perceive by the practitioners, (ii) understanding interactions between humans and technological interfaces, and (iii) understanding the relevant soft skills for the navigation functions. To address these topics, data was collected from expert practitioners such as navigators, pilots and instructors, thru semi structured interviews and questionnaires. The mains contribution of this study lies in presenting a framework of maritime navigation, exploring the control processes in the different levels of the maritime socio-technical system. In the view of safe operations, interactions between stakeholders are clarified, trying to determine how they influence safe navigation. This systemic view is then analysed from the perspective of the ship, considering it as a Joint-cognitive system (JCS). It is proposed that this JCS comprises 5 control levels: reactive, proactive, planning, strategic and political-economical. Planning is considered a fundamental process in the maritime Socio-technical system, because it facilitates the interactions between the different control level. It also increases the integrity of communications and enhances the predictability of the different control agents. New directions are proposed to improve the design of navigation system, recommending new roles for human and automated agents, and presenting a new conceptual navigation display.info:eu-repo/semantics/publishedVersio

Quantifying protocol evaluation for autonomous collision avoidance

Collision avoidance protocols such as COLREGS are written primarily for human operators resulting in a rule set that is open to some interpretation, difficult to quantify, and challenging to evaluate. Increasing use of autonomous control of vehicles emphasizes the need to more uniformly establish entry and exit criteria for collision avoidance rules, adopt a means to quantitatively evaluate performance, and establish a “road test” for autonomous marine vehicle collision avoidance. This paper presents a means to quantify and subsequently evaluate the otherwise subjective nature of COLREGS thus providing a path toward standardized evaluation and certification of protocol-constrained collision avoidance systems based on admiralty case law and on-water experience. Notional algorithms are presented for evaluation of COLREGS collision avoidance rules to include overtaking, head-on, crossing, give-way, and stand-on rules as well as applicable entry criteria. These rules complement and enable an autonomous collision avoidance road test as a first iteration of algorithm certification prior to vessels operating in human-present environments. Additional COLREGS rules are discussed for future development. Both real-time and post-mission protocol evaluation tools are introduced. While the motivation of these techniques applies to improvement of autonomous marine collision avoidance, the concepts for protocol evaluation and certification extend naturally to human-operated vessels. Evaluation of protocols governing other physical domains may also benefit from adapting these techniques to their cases. Keywords: COLREGS; Autonomous collision avoidance; Human–robot collaboration; Marine navigatio

Quantifying protocol evaluation for autonomous collision avoidance

Collision avoidance protocols such as COLREGS are written primarily for human operators resulting in a rule set that is open to some interpretation, difficult to quantify, and challenging to evaluate. Increasing use of autonomous control of vehicles emphasizes the need to more uniformly establish entry and exit criteria for collision avoidance rules, adopt a means to quantitatively evaluate performance, and establish a “road test” for autonomous marine vehicle collision avoidance. This paper presents a means to quantify and subsequently evaluate the otherwise subjective nature of COLREGS thus providing a path toward standardized evaluation and certification of protocol-constrained collision avoidance systems based on admiralty case law and on-water experience. Notional algorithms are presented for evaluation of COLREGS collision avoidance rules to include overtaking, head-on, crossing, give-way, and stand-on rules as well as applicable entry criteria. These rules complement and enable an autonomous collision avoidance road test as a first iteration of algorithm certification prior to vessels operating in human-present environments. Additional COLREGS rules are discussed for future development. Both real-time and post-mission protocol evaluation tools are introduced. While the motivation of these techniques applies to improvement of autonomous marine collision avoidance, the concepts for protocol evaluation and certification extend naturally to human-operated vessels. Evaluation of protocols governing other physical domains may also benefit from adapting these techniques to their cases. Keywords: COLREGS; Autonomous collision avoidance; Human–robot collaboration; Marine navigatio

Providing Nautical Chart Awareness to Autonomous Surface Vehicles

Autonomous surface vessels (ASVs) have many applications in both military and civilian domains including mine countermeasure, seafloor mapping, and physical oceanography. However, to act as effective tools, ASVs require high levels of autonomy. Currently, many commercially available ASVs have static mission plans with minimal awareness of their environment, which results in a labor intensive approach that does not scale to management of multiple vehicles. In this research, ASV autonomy was increased through the development of an intelligent mission planner and a real-time obstacle avoidance system utilizing Electronic Nautical Charts (ENCs), which describe known hazards in the marine environment without suffering from the challenges of real-time sensor processing. A new algorithm called Depth-Based A* was developed as the mission planner, where the nominal A* search algorithm was expanded by utilizing a novel cost function that balances driving in the channel with taking the most direct route on an ENC-derived cost map. Although charted obstacles can typically be avoided through mission planners, there is still an advantage in having the code do this. However, since it enables even higher levels of autonomy (e.g., “go in this area, but avoid all known obstacles”) they must still be accounted for in real time as other behaviors (i.e., avoiding uncharted obstacles or vessels) might cause the ASV to deviate from the planned path. The reactive obstacle avoidance system developed in this research reorganizes the ENC into a quick-search database where ENC-based obstacles in the ASV’s proximity are determined and avoided. These algorithms were tested with both a Seafloor System EchoBoat and ASV Global C-Worker 4 in simulation and in the field using an EchoBoat, where they avoided both concave and convex polygons. The algorithms developed in this research provide the ASV with a higher level of autonomy, potentially allowing for the same number of human operators to manage more ASVs

Investigation of the Influences of Human Error Factor in Maritime Transportation

Marine transport has a vital role in people and cargo transport across the world, where, more than 90% of the world’s cargo transports by merchant ships. Marine transport industry is considered one of the huge and high-risk industries. This clarify why safety is one of the imperatives of the maritime industry and which highly affect the success and efficient exist of this industry. Therefore, reducing the associate risks and improving maritime safety are of the essential requirements for main marine transport industry. There are many parameters contributing into improving maritime safety and reducing the associate risks of accidents. Efforts are presented and attention is given by shipping industry toward that. This is mainly by focusing in safety regulations, improving ship’s structural design and construction methodologies and techniques and by improving ship’s systems operation and reliability. Accordingly, improvements in ship’s hull design, building processes and methodologies; utilization of advanced technologies and equipment and improving ships legislation and regulations have been clearly noticed. Instead of that, the maritime casualty rate and accidents are still high. This is because ship structure and system reliability are a relatively small part of the safety equation. Where, ship safety is highly affected by human actions as the majority of maritime accidents are consequences of human error. Meanwhile, human factors have the largest share in marine accidents, where, more than 80% of marine accidents have been caused by human error. Therefore, human error is one of the most important issues concerning global maritime communities and it is one of the important factors in the assessment of maritime accidents. Several studies are conducted to assess the contribution of human factors in maritime accidents in order to reduce the overall number of marine accidents. The study of human behavior in the field of marine activities is challenging task due to the difficulties, expenses, and time-consuming factors. Moreover, there is lack of information on the role of human in marine accidents. This study aiming at presenting the effect of human errors in the overall maritime safety. This is through analyzing 98 of ships accidents happened during 2014-2017 to investigate the main parameters contributing in these accidents, identify human error related causes and estimate the overall contribution of the human error causes to the occurrence of these accidents. The results of the analysis indicated that 75% of the causes of the registered accidents were due to human error. In order to provide details about the contribution of the human error to the overall ship accidents causes, analysis to the reported accidents by European Marine Casualty Information Platform from 2011-2017 for cargo ships, fishing vessels, passenger ships and service ships. The results of survey indicated high contribution of human error to the causes of ships accidents, where it represents: • 62.2% of the total of 156 accidental events analyzed of service ships • 60.8% of the total of 781 accidental events analyzed of cargo ships • 54.4% of the total of 338 accidental events analyzed of fishing vessels • 51.4% of the total of 319 accidental events analyzed of passenger ships Moreover, a detailed analysis of a collision case study between Kuwaiti oil tanker “Kaifan” and cargo ship “Unison Star” collided at Chittagong - Bangladesh anchorage area (2017). The analysis of the collision case study conducted using step-by-step events evaluation technique and a systematic process for accident investigation based on comprehensive and multi linear description of events sequences using STEP methodology to investigate rout causes of the collision and identify the contribution of human error causes. The results of investigation clearly prove the contribution of human error as a main factor led to collision. In addition, this thesis investigates collision avoidance procedures, which use a dedicated negotiation and communication system to optimize locally found trajectories according to a global performance measure. This is by introducing, discussing and analyzing of three ship collisions avoidance algorithms based on multiple‐ship situations, which are the Distributed Local Search Algorithm (DLSA), the Distributed Tabu Search Algorithm (DTSA) and the Distributed Stochastic Search Algorithm (DSSA) Furthermore, in experimental results, compared to DLSA and DTSA, DSSA produced good results, such as decreasing the number of messages. Therefore, DSSA enables ships to exchange significantly fewer messages than DLSA and DTSA then I developed a mathematical algorithm for the risk assessment and collision avoidance and calculating collision risk index and present a criteria to be applied and present the MATLAB code which used to calculate collision risk index. Finally, the thesis ended by detailed conclusions, remarks and recommendations to improve maritime safety and improving human factor by eliminating the concerned associated errors.Chapter 1 Introduction 1 1.1 Research Motivation and Problem Identification 1 1.2 Ship Accident Types 2 1.3 Human Error Definition 4 1.4 Research Questions 5 1.5 Aim and Objectives 6 1.6 Contribution 7 1.7 Thesis Structure 8 Chapter 2 Literature Review 10 2.1 Introduction 10 2.2 Investigation the Causes of Marine Accident 10 2.2.1 Gained Points from Literature Review 14 2.2.2 Human Errors Contribution on Maritime Accidents 14 2.2.3 Gained Points from Literature Review of Human Error Contribution 17 2.3 Marine Accident Investigation Methods 18 2.3.1 Events and Causal Factors Charting (ECFC) 18 2.3.2 STEP (Sequential Timed Events Plotting) 21 2.3.3 Fault Tree Analysis (FTA) 22 2.3.4 Event Tree Analysis 23 2.3.5 Root Cause Analysis 25 2.3.6 SHELL Analysis Method 27 2.3.7 Step-By-Step Approach 29 Chapter 3 Analysis and Investigation of Human Error Influences on Maritime Transportation 30 3.1 Introduction 30 3.2 Analysis of KOTC’s Ships Accidents 31 3.2.1 Statistical Survey of KOTC’s Ships Accidents 31 3.2.2 Human Error Types on Ship Accidents 36 3.3 Analysis of Ship Accidents Types and Causes Reported By EMCIP 39 3.4 Human Error Contribution to the Overall Ships Accidents (2011 – 2017) 42 Chapter 4 Detailed Analysis Methodology of KOTC Ship Accident Case Study 55 4.1 Introduction 55 4.2 Description of Chittagong – Bangladesh Port 55 4.3 Description of Vessels 59 4.3.1 Kaifan Oil Tanker 59 4.3.2 Unison Star Bulk Carrier 62 4.4 Collision Case Study 64 4.4.1 Course of Events 64 4.4.2 Comprehensive and Multi-Linear Description of the Accident Process 68 4.4.3 Collision Consequences 71 4.4.4 Results and Discussion 73 4.5 Recommendation 75 Chapter 5 Ships Collision Avoidance Algorithm 77 5.1 Introduction 77 5.2 Framework and Terminology 78 5.2.1 Framework 78 5.2.2 Terminology 80 5.2.3 Cost and Improvement 84 5.3 Distributed Local Search Algorithm 87 5.3.1 Reason of Selection 87 5.3.2 DLSA Procedure 88 5.3.3 Results 91 5.4 Distributed Tabu Search Algorithm 92 5.4.1 Reason of Selection 92 5.4.2 DTSA Procedure 93 5.4.3 Simulation 96 5.4.4 Results 101 5.5 Distributed Stochastic Search Algorithm 101 5.5.1 Reason of Selection 101 5.5.2 DSSA Procedure 102 5.5.3 Simulation 103 5.5.4 Results 105 5.6 Comparative Analysis between Distributed Algorithms 106 5.6.1 Results 109 Chapter 6 Conclusions and Recommendations 110 6.1 Conclusions 110 6.2 Recommendation 115 Acknowledgement 118 References 119 Appendix (A) Mathematical Collision Avoidance Algorithm 125Docto