The development of innovative, complex marine systems, such as autonomous ship concepts, has led to risk-based approaches in design and operation that provide safety level quantification and continuous risk assessment. The existing approaches to dynamic risk assessment mainly aim at updating accident probabilities for specific risk scenarios, based on knowledge of system operation and failure, as well as on past accident and failure information. However, for innovative marine systems that include complex interactions, our ability to identify anything that might go wrong is very limited, which may lead to unidentified risks, and failure data may not be available. This paper presents the foundations of a framework for dynamic risk assessment, which is equally applicable to manned and autonomous ships and mainly relies on information about the safe operational envelope and real-time information regarding deviations from safety. Inspiration is drawn from how the biological immune system identifies the risk of infection in a dynamic environment. The objective is to show the feasibility and benefits of our approach for quantifying the operational risk of marine systems. This paper provides the conceptual basis for developing ship specific applications and describes a process for dynamic risk assessment that is methodologically based on artificial immune systems. To demonstrate the implementation of our framework, we described, an illustrative example that involves a ship in a grounding scenario. The results show that the bio-inspired assessment process and risk description can reflect the changes of the risk level of a marine system.

In case of a ship emergency situation and during its evolvement that might result in an evacuation, the master and the bridge command team of a ship have to continuously assess risk. This is a very complex procedure, as crucial decisions concerning safety are made under time pressure. The use of a decision-support tool would have a positive effect on their performance, resulting in an improvement in the way ships are evacuated. The purpose of this paper is to present the PALAEMON smart risk assessment platform (SRAP). SRAP is a real-time risk assessment platform developed to assist the decision-making process of the master and bridge command team of a ship regarding the evacuation process. Its purpose is to provide decision support for the following aspects: (1) the decision to sound the general alarm (GA) following an accident, (2) monitoring the progress of the mustering process in order to take any additional actions, and (3) the decision to abandon the ship or not. SRAP dynamically assesses the risk to the safety of the passengers and crew members in the different phases of the evacuation process, so one model in the form Bayesian networks (BNs) was developed for each stage of the evacuation process. The results of a case study that was implemented reflect how various parameters such as injuries, congestion, and the functionality of the ship’s systems affect the outcome of each model.

Research on autonomous ships has led to several ideas about how they could be operated in terms of level of automation and human presence. However, all seem to point towards systems that will be more complex due to issues such as the tighter integration with software and the interactions with humans-in-the-loop. These ships will likely be completely different compared to conventional ones, which puts into question the usefulness of our current understanding of risk and how it is managed for ensuring safe operation. By a critical review of the literature, this paper highlights the need for advanced methods that will combine machine learning and simulation for dynamically assessing risk in a life cycle context and effectively transferring acquired operational knowledge back to the design phase. The main objective of this paper is to describe a novel life cycle risk framework for developing algorithms inspired by the biological immune system, which provides lifetime protection from harmful pathogens. This framework can be used to construct machine learning algorithms for dynamic risk monitoring and adaptive risk control that address different types of risk, while enabling faster future response to previously unencountered risks and operational feedback to design through learning. We demonstrate the feasibility of our approach in a specific maritime context with a case study on collision risk identification. Considering the lack of experience for autonomous ships, the benefit of our immune-inspired approach is that it departs from the classic risk scenario concept and that it does not rely on safety performance data for identifying risk factors and training the algorithms. We suggest this framework is particularly suitable for autonomous ships with high levels of autonomy, although applicable to conventional ships as well, as it can contribute to empowering them with risk awareness and the capability to deal with any risk environment.

The digitalization of the maritime sector is continuously growing, leading to increased automation, such as, the development of autonomous vessels. The Autonomous Passenger Ship (APS) is a characteristic instantiation of this development, aiming to transport people on urban waterways. Although emerging technologies deployed in such APS aim to facilitate the functions and operations of the navigation and communication systems, various safety and security risks are inherent to the communication infrastructure due to their interconnectivity. The aim of this work is to study the safety and cyber security of the communication system of an APS, namely the MilliAmpere2 APS. The six step model (SSM) is utilized to facilitate the joint analysis. The application of the SSM enables, among others, the capturing of relationships between cyber attacks and component failures, the assessment of safety and cyber security countermeasures, as well as, the synergies between them. It has been found that most countermeasures in both categories are reinforcing or are conditionally dependent on each other, while few antagonize each another. These findings will allow for improved design and implementation of integrated safety and security management solutions.

The maritime industry has been increasingly trying to incorporate more proactive approaches for safety both in the design and operation phases of the life-cycle of ships. However, learning from experience has remained a mostly reactive exercise in the aftermath of major marine accidents and its effectiveness is questionable considering the recurrence of accidents with common contributing factors. In addition, the development of autonomous ships that will be radically different and more complex systems begs the question whether learning needs to be redefined to ensure an adequate level of safety performance throughout their life-cycle. In this context, this paper presents a novel framework for managing risk knowledge inspired by the way the biological immune system remembers not previously encountered threats to implement a faster immune response when the same or similar threats are faced in the future. In this novel framework learning is described as a distributed life-cycle process with interacting components in the design and operation phases. As this framework is equally applicable to conventional and autonomous ships, the latter are considered an opportunity to change the way the maritime industry thinks about learning from experience.

Piracy and armed robbery continue to pose significant security threats to the shipping industry. This paper presents a real-time threat assessment and crisis classification tool for piracy or armed robbery incidents. The tool is part of a crisis classification module that addresses various categories of security threats. This module is currently being developed as part of the EU-funded research project ISOLA, which aims to introduce an intelligent security superintendence ecosystem. The ecosystem is designed to complement the existing ship security processes and measures applied onboard passenger ships. The tool operates by providing real-time threat classification and subsequent warnings by analysing data collected from the ship’s legacy systems and installed sensors with the utilisation of Bayesian probabilistic techniques, particularly Bayesian Networks. The BN model developed for this purpose is thoroughly examined, and its validation is presented through indicative case studies involving piracy and armed robbery. The main objective is to improve situational awareness, enhance vigilance and early threat detection, and support the decision-making process for the Master and crew, especially under time-sensitive circumstances and stressful conditions.