Maritime autonomous surface ships (MASS) need to be sufficiently safe to gain commercial acceptance. Collision avoidance strategies in such MASS should comply with the International Regulations for Preventing Collision at the Sea (COLREGs). According to the COLREGs, collision risk assessment, which determines the optimal positioning and timing via all available means appropriate to the prevailing circumstances and conditions, is crucial for preventing collisions. However, existing collision risk assessment methods do not consider all vital factors for the COLREGs rules compliant collision avoidance. We propose a collision risk inference system for MASS that complies with COLREGs vital rules for collision avoidance as follows: 1) actions to avoid collision are defined according to the degree of danger, and a suitable response distance is determined; 2) a collision risk index according to the enlarged ship domain based on the designated response distance by each level is set; 3) all vital factors of the COLREGs rules compliant collision avoidance are extracted as the data when the ship domain enlarged by each level is overlapped; 4) the collision risk inference system is developed by learning extracted data via the adaptive neuro fuzzy inference system. In contrast to existing research, the proposed system considers all vital variables in the COLREGs rules compliant collision avoidance guidelines, thereby improving the timings and positionings of the potential collision warning. Consequently, it could secure more time for decision making to take necessary collision prevention action.

A maritime autonomous surface ship (MASS) ensures safety and effectiveness during navigation using its ability to prevent collisions with a nearby target ship (TS). This avoids the loss of human life and property. Therefore, collision avoidance of MASSs has been actively researched recently. However, previous studies did not consider all factors crucial to collision avoidance in compliance with the International Regulations for Preventing Collisions at Sea (COLREGs) Rules 5, 7, 8, and 13–17. In this study, a local route-planning algorithm that takes collision-avoidance actions in compliance with COLREGs Rules using a fuzzy inference system based on near-collision (FIS-NC), ship domain (SD), and velocity obstacle (VO) is proposed. FIS-NC is used to infer the collision risk index (CRI) and determine the time point for collision avoidance. Following this, I extended the VO using the SD to secure the minimum safe distance between the MASS and the TS when they pass each other. Unlike previous methods, the proposed algorithm can be used to perform safe and efficient navigation in terms of near-collision accidents, inferred CRI, and deviation from the course angle route by taking collision-avoidance actions in compliance with COLREGs Rules 5, 7, 8, and 13–17.

Ships transport large volumes of cargo, and are therefore major contributors to the global economy. Ship collisions can cause significant economic losses. Path-planning algorithms can prevent such collisions by suggesting the optimal path for navigation. Conventional path-planning algorithms are disadvantageous, because they do not consider the navigation practices followed by experienced navigators. Therefore, in this study, we developed the requirements for optimal local path planning of autonomous ships by considering the open sea, restricted waters, and two-ship and multi-ship interactions, in addition to the navigation practices adopted by navigators and rules in COLREGs part B. First, the navigation practices under various scenarios were collected. Subsequently, these practices were linked to COLREGs part B to extract the key rules and keywords related to collision avoidance. Finally, the requirements for generating the optimal local path were drafted based on the rules and keywords. The utility of the requirements was demonstrated by applying them to representative path-planning algorithms for the timely and accurate evaluation of their effectiveness. The proposed requirements can be utilized to improve the existing path-planning algorithms and develop superior algorithms in the future.

To reduce the risk of collision in territorial sea areas, including trade ports and entry waterways, and to enhance the safety and efficiency of ship passage, the International Maritime Organization requires the governing body of every country to establish and operate a vessel traffic service (VTS). However, previous studies on risk prediction models did not consider the locations of near collisions and actual collisions and only employed a combined collision risk index in surveillance sea areas. In this study, we propose a regional collision risk prediction system for a collision area considering spatial patterns using a density-based spatial clustering of applications with noise (DBSCAN). Furthermore, a fuzzy inference system based on a near collision (FIS-NC) and long short-term memory (LSTM) is adopted to help a vessel traffic service operator (VTSO) make timely optimal decisions. In the local spatial pattern stage, the ship trajectory was determined by identifying the actual-collision and near-collision locations simultaneously. Finally, the system was developed by learning a sequence dataset from the extracted trajectory of the ship when a collision occurred. The proposed system can recommend an action faster than the fuzzy inference system based on the near-collision location. Therefore, using the developed system, a VTSO can quickly predict ship collision risk situations and make timely optimal decisions at dangerous surveillance sea areas.

According to International Regulations for Preventing Collision at Sea, collision avoidance started from assessing the collision risk. In particular, the radar was mentioned as suitable equipment for observation and analysis of the collision risk. Thus, many researches have been conducted by utilizing the radar. Fuzzy Inference System based on Type-1 Fuzzy Logic (T1FIS) using Distance to Closest Point of Approach ( $D_{CPA}$ ) and Time to Closest Point of Approach ( $T_{CPA}$ ) computed via the radar has been largely used for assessing the collision risk. However, the T1FIS had significant limitations on the membership function not including linguistic and numerical uncertainties. In order to solve the issue, we developed the Fuzzy Inference System based on Interval Type-2 Fuzzy Logic (IT2FIS) as follows: (i) the T1FIS was selected among proposed methods based on the type-1 fuzzy logic; (ii) we extended the T1FIS into the IT2FIS by gradually increasing the Footprint of Uncertainty (FOU) size taking into consideration symmetry, and (iii) numerical simulations were conducted for performance validation. As a result, the IT2FIS using the FOU size “±5%” (i.e., interval 10% between upper membership function and lower membership function) not only computed the appropriate and linear collision risk index smoothly until near-collision situation but also help to overcome uncertainties that exist in real navigation environments.

The criticality of collision-avoidance technology for ensuring safe navigation of autonomous ships necessitates diverse testing scenarios that reflect complex maritime environments. However, previous testing scenarios, often based on virtual trajectories or simplified encounters, have shown limitations in adequately representing real-world conditions. This study proposes a novel framework for developing collision-avoidance testing scenarios based on actual collision cases. The framework consists of three stages: collision case collection, trajectory extraction, and scenario development. Relevant data were extracted from selected cases, and the trajectories of ships influencing the collision were combined to reconstruct the circumstances at the time of the incident. Encounter situations were then diversified by altering the roles and positions of own and target ships, and finally systematically categorised into a structured testing set. Unlike previous testing scenarios, the developed scenarios exhibit distinctive characteristics derived from actual collision cases, including situations where navigation rules cannot be strictly applied, dynamic encounters, speed variations, and environmental conditions. By reflecting real maritime environments, these scenarios provide a solid basis for validating and improving collision-avoidance algorithms. The proposed framework is expected to contribute not only to the advancement of autonomous-ship technology but also to the enhancement of maritime safety.