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.

Ship route planning is a crucial activity that must consider not only the safety of the ship but also the safe passage of nearby ships in the same space and time. This study aims to provide general route-planning guidance to shipping traffic by improving conventional sampling-based route-planning algorithms in accordance with the maritime environment from a ship operator’s perspective. The obstacle safety margin in a marine environment can be reflected in a binary image map space based on an electronic navigational chart. Consequently, an initial route was created using the probabilistic road map (PRM) algorithm in the configured map space to increase the speed of the conventional sampling-based route-planning algorithm. Based on the initial route created, a designated space—that is, a multi-elliptical area—was created to limit the route-search range. After searching the final route in the designated space based on the rapidly-exploring random tree (RRT*) algorithm, optimal route planning could be achieved by generating a collision-free space graph to remove unnecessary nodes from the searched final route. The simulation results showed that the route was shortened by approximately 33.7 km compared with the conventional RRT* algorithm, and the calculation time was shortened by approximately 2.5 times.

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.

Joo-Sung Kim. IJFIS 2017;17:279-88. https://doi.org/10.5391/IJFIS.2017.17.4.279

Safe navigation of maritime autonomous surface ships (MASS) relies on two capabilities: path planning and collision avoidance. This review surveys classical algorithms and modern AI techniques for embedding the International Regulations for Preventing Collisions at Sea (COLREGs) into autonomous navigation. We organise prior work into three families—classical search/optimisation, real-time reactive methods, and learning-based approaches—and discuss their strengths and limitations with respect to rules compliance, computational cost, and onboard constraints. Building on these insights, we outline a large-language-model framework, Navigation-GPT, which couples reasoning-and-acting (ReAct) prompting with low-rank adaptation (LoRA). We further propose a three-phase deployment roadmap for MASS: core model integration, domain fine-tuning, and integrated operations. The paper concludes with open challenges and research directions toward reliable, explainable, and fully compliant MASS navigation.

Joo-Sung Kim, and Jung Sik Jeong. Int. J. Fuzzy Log. Intell. Syst. 2015;15:268-76. https://doi.org/10.5391/IJFIS.2015.15.4.268