A Remote Operator’s Ship Collision Avoidance Performance Evaluation Model: Comparison Between Human and AI Decisions in the Remote Operation Simulation Training

Ship's collision avoidance is a skill that masters of merchant marine vessels have acquired through years of experience and that makes them feel at ease to guide their ship out from danger quickly compared to inexperienced officers. Case based reasoning (CBR) uses the same technique in solving tasks that needs reference from variety of situations. CBR can render decision-making easier by retrieving past solutions from situations that are similar to the one at hand and make necessary adjustments in order to adapt them. In this paper, we propose to utilize the advantages of CBR in a support system for ship's collision avoidance while using fuzzy algorithm for its retrieval of similar navigational situations, stored in the casebase, thus avoiding the cumbersome tasks of creating a new solution each time a new situation is encountered. There will be two levels within the Fuzzy-CBR. The first level will identify the dangerous ships and infer the new case. The second level will retrieve cases from casebase and adapt the solution to solve for the output. While CBR's accuracy depends on the efficient retrieval of possible solutions to be adapted from stored cases, fuzzy algorithm will improve the effectiveness of solving the similarity to a new case at hand.

A real-time multi-ship collision avoidance decision-making system for autonomous ships considering ship motion uncertainty

The reliability of collision avoidance systems for Maritime Autonomous Surface Ships is one of the most critical factors for their safety. In particular, since many ship collisions occur in coastal areas, it is crucial to ensure the reliability of collision avoidance algorithms in geographically limited coastal waters. However, studies on maritime autonomous surface ships collision avoidance algorithms mainly focus on the traffic factor despite the importance of the geographic factor. Therefore, this study presents a methodology for establishing a practical collision avoidance system test bed, considering the geographic environment. The proposed methodology is a data-driven approach that objectively categorizes collision risk situations by extracting these risks using Automatic Identification System (AIS) and Electronic Navigational Chart (ENC) data, followed by clustering algorithms. Consequently, the research results present a direction for establishing test beds from the perspective of geographic and traffic factors.

COLREGs and their application in collision avoidance algorithms: A critical analysis

The exploration of ship collision avoidance behavior characteristics can provide a theoretical basis for ship collision risk assessment and collision avoidance decision-making, which is significant for ensuring maritime navigation safety and the development of intelligent ships. In order to scientifically and effectively analyze the characteristics of ship collision-avoidance behavior and to seek the intrinsic connections among ship collision-avoidance behavior feature parameters(CABFPS), this study proposes a method that combines the Apriori algorithm and complex network theory to mine ship collision-avoidance behavior characteristics from massive AIS spatiotemporal data. Based on obtaining ship encounter samples and CABFPS from AIS data, the Apriori algorithm is used to mine the association rules of motion parameters, and the maximum mutual information coefficient is employed to represent the correlation between parameters. Complex networks of CABFPS for different encounter situations are constructed, and network topological indicators are analyzed. Mutual information theory is applied to identify key parameters affecting ship collision- avoidance behavior under different situations. The analysis using actual AIS data indicates that during navigation, the relationships among various parameters are closely linked and prone to mutual influence. The impact of CABFPS on ship collision-avoidance actions varies under different encounter scenarios, with relative distance and DCPA having the greatest influence on ship collision-avoidance actions. This method can comprehensively and accurately mine the correlations between CABFPS and the influence mechanism of parameters on collision-avoidance actions, providing a reference for intelligent ship navigation and the formulation of collision-avoidance decisions.

In order to improve the effectiveness of intelligent collision avoidance decision of ships, a collision avoidance decision model is established with the objective of guaranteeing the safety of ship navigation and reducing the loss of voyage, taking real-time data as the input quantity and the requirements of international maritime collision avoidance rules and the common practice of seafarers as the constraints, and MOEA/D (Multi-objective Evolutionary Algorithm Based on Decomposition) algorithm is used to optimize the objective function and calculate the optimal steering angle and steering timing. The simulation results show that the decision can meet the safety and economic requirements of ship collision avoidance under different encounter situations and can provide some reference for ship collision avoidance decision.

Autonomous collision avoidance method for MASSs based on precise potential field modelling and COLREGs constraints in complex sailing environments

The advancement of Maritime Autonomous Surface Ships (MASS) relies heavily on data-driven knowledge discovery and situational awareness to ensure safe and efficient navigation. This PhD by Published Works comprises five peer-reviewed journal papers, contributing to the integration of Artificial Intelligence (AI), machine learning, deep learning, and Bayesian Network (BN) into key areas of maritime safety and security. The research aims to enhance risk assessment, piracy incident analysis, ship trajectory prediction, and autonomous route planning, forming a cohesive framework for intelligent decision-making in MASS operations. The first contribution focuses on data-driven risk analysis of global maritime accidents, employing a data-driven BN model to quantify accident risks, identify critical factors, and enhance safety management strategies. The second contribution addresses spatio-temporal piracy pattern analysis, utilising AI-driven methodologies to detect high-risk zones and extract patterns of piracy incidents, thereby improving security measures and navigational decision-making for MASS. The third and fourth contributions focus on ship trajectory prediction and route planning, which are crucial for autonomous navigation. A systematic review of machine learning and deep learning-based trajectory prediction methods highlights the effectiveness of data-driven models over traditional approaches. Further, an unsupervised route planning model integrates Automatic Identification System (AIS) data and machine learning techniques, enabling autonomous vessels to adapt dynamically to changing maritime conditions. The final contribution refines AIS-based trajectory prediction frameworks, leveraging machine learning methodologies to improve predictive accuracy and enhance collision avoidance capabilities. By synthesising insights from these studies, this research establishes a holistic framework for data-driven knowledge discovery and situational awareness in MASS. The integration of this research content and outcomes ensures that autonomous vessels can navigate safely, mitigate operational risks, and enhance adaptability to evolving maritime conditions. This study advances AI-driven autonomous navigation by bridging data-driven knowledge discovery with real-time maritime decision-making, providing a resilient and adaptive foundation for next-generation MASS technology.

This paper provides a comprehensive overview of the current developments and challenges of Maritime Autonomous Surface Ships (MASS), defined by the International Maritime Organisation (IMO) as commercial vessels capable of operating with minimal or no onboard crew. The study aims to clarify the technological, regulatory, and operational factors shaping the transition toward autonomous shipping and to identify the key challenges that must be addressed for successful implementation. It examines IMO regulations and the established degree of autonomy, followed by a review of existing MASS prototypes, summarising their power systems, propulsion technologies, degree of autonomy, and development timelines. The paper then analyses the integration of critical technologies, including collision avoidance, path-following, and path-planning systems, highlighting their potential and limitations. Furthermore, it discusses the economic implications, safety concerns, and cybersecurity risks associated with autonomous operations, as well as the emerging regulatory frameworks guiding MASS deployment. By synthesising insights across these domains, this study contributes to a clearer understanding of the current state and prospects of MASS, offering a foundation for further research, policy development, and industry innovation.

With the development of Maritime Autonomous Surface Ships (MASS), considerable research is undertaken to secure their safety. One of the critical aspects of MASS is collision avoidance, and multiple collision avoidance algorithms have been developed. However, due to various reasons, collision avoidance of autonomous merchant vessels appears to be far from resolved. With this study, we aim to discuss the current state of Collision Avoidance Methods (CAMs) and the challenges lying ahead—from a joint academic and practical point of view. To this end, the key Rules from International Regulations for Preventing Collisions at Sea (COLREG) have been reviewed with a focus on their practical application for MASS. Moreover, the consideration of the COLREG Rules in contemporary collision avoidance algorithms has been reviewed. The ultimate objective is to identify aspects of COLREG requiring additional attention concerning MASS developments in terms of collision avoidance. Our conclusions indicate that although a lot of progress has been achieved recently, the feasibility of CAMs for MASS remains questionable. Reasons for so are the ambiguous character of the regulations, especially COLREG, as well as virtually all existing CAMs being at best only partly COLREG-compliant.

Operations in the Next Generation Air Transportation System (NextGen) are planned to have a reduced dependency on primary surveillance radar and use Automatic Dependent Surveillance - Broadcast (ADS-B) to provide aircraft state information to Air Traffic Control (ATC) and onboard collision detection and avoidance systems. With the ongoing integration of Unmanned Aircraft Systems (UAS) in the National Airspace System (NAS), ADS-B is expected to become the main surveillance input to UAS collision avoidance. Traffic Alert and Collision Avoidance System (TCAS) is unable to meet requirements in terms of adaptability to future operations with reduced separation margins, addition of new user groups (e.g., UAS), and support of additional surveillance inputs. Airborne Collision Avoidance System (ACAS X) is being designed to meet these requirements and to increase surveillance, alerting, and resolution performance. The RTCA has released Minimum Operational Performance Standards (MOPS) to provide system and operational performance characteristics that ADS-B must provide. This paper analyzes recorded ADS-B data from test flights that were performed in the Continental U.S. (CONUS) and verifies measured performance with specified operational characteristics in terms of coverage, accuracy, and integrity.

It is essential to investigate the path planning and tracking problem for maritime autonomous surface ships (MASS). This study proposed an automatic collision avoidance method based on the separation and recovery mechanism with formation of MASS (F-MASS). First, the marine environment model utilized S-57 electric navigation chart (ENC) data for construction. The Scan-searching method was proposed for multipath and path-optimized redistribution method was developed to address MASS congestion. The formation pattern transformation was settled by separation and recovery mechanism. Considered the ‘unmanned–manned’ encounter situation, formulated avoidance strategies for both the MASS formation and manned ships encounters. Finally, the simulation experiment was carried out in the sea near Zhoushan port to verify the effectiveness of the algorithm. The results indicated that the method can satisfy the integrated operation of collision avoidance in a complex environment and can offer effective collision avoidance strategies for F-MASSs navigating in complex marine environments.

Coordinated multi-stage and multi-objective optimization approach for ship collision avoidance decision-making

To meet the higher requirements of Maritime Autonomous Surface Ships (MASS) development for autonomous collision avoidance technologies, this paper proposes a multi-ship autonomous collision avoidance decision-making algorithm based on MER-D3QN (Dueling Double Deep Q-Network with mixed experience replay). Firstly, a Reinforcement Learning (RL) test system based on the Markov Decision Process (MDP) with the heterogeneous multi-agent system (HMS) test environment is established to determine the distributed learning-execution mechanism. Secondly, a reward function is designed by considering various factors that affect ship collision avoidance from a global perspective. Meanwhile, the D3QN network is used to train the algorithm model with mixed experience replay technology, which can accelerate the model's convergence speed. Finally, the algorithm's performance is validated using the classic Imazu problem as the testing set. The experimental results show that this algorithm enables all ships to complete collision avoidance tasks safely and efficiently in compliance with the International Regulations for Preventing Collisions at Sea (COLREGs) requirements. Furthermore, the trained model can be applied in the HMS with any number of agents, providing a new research idea for the field of ship autonomous navigation.

Maritime Autonomous Surface Ship (MASS) is promoted as the future of intelligent shipping. While autonomy technologies offer a solution for MASS, they have also resulted in new challenges for performance validation. To address this, a scenario-based validation method to test the autonomous collision avoidance system is proposed in this paper, including mining ship encounter scenarios from massive historical AIS data and randomly generated virtual test scenarios according to the parameter probability distributions from the collected real scenarios, as well as the final experiments: a total of 2900 generated scenarios including single ship and multi-ship encounter situations are created and applied to conduct testing experiments on the further assessment of our collision avoidance algorithm. The results indicate that the proposed method has the ability to quickly create appropriate testing scenarios according to AIS records, which are helpful to catch potential defects in a collision avoidance algorithm of MASS and to further analyze its navigating features. As a result, the research forms a systematic set of validation procedures from data gathering to practical experiments conduction, incorporating both the real statistics and the random generation method.