@article{Ishii_Tamaru_2026,
	author = {Ishii, Mikihisa and Tamaru, Hitoi},
	title = {Incorporating Probabilistic Mutual Interactions in Simulation-Based Safety Evaluation of Maritime Autonomous Surface Ships},
	journal = {TransNav, the International Journal on Marine Navigation and Safety of Sea Transportation},
	volume = {20},
	number = {2},
	pages = {387-395},
	year = {2026},
	url = {./Article_Incorporating_Probabilistic_Mutual_Ishii,78,1668.html},
	abstract = {This paper proposes a â€sProbabilistic Reactive Target Model” that generates avoidance behaviors for target ships to evaluate the collision avoidance algorithms of Maritime Autonomous Surface Ships (MASS) in a realistic simulation environment. Focusing on the â€sshift of the Points of Potential Collision (PPC)” resulting from collision avoidance maneuvers in one-on-one head-on situations, we conducted indirect probabilistic modeling using AIS data. Specifically, we constructed a state transition probability model by estimating the directional probability of the PPC shifting to either the starboard or port side using a linear binary classification model, and by estimating the parameters of the passing distance distribution for each side using a neural network, assuming a log-normal distribution. Furthermore, by iteratively sampling and evaluating transitions to target states that follow this model, we demonstrated that it is possible to generate behaviors in a simulation environment where target ships react to the movements of the MASS.},
	doi = {10.12716/1001.20.02.13},
	issn = {2083-6473},
	publisher = {Gdynia Maritime University, Faculty of Navigation},
	keywords = {Safety of Navigation, Risk Assessment, e-Navigation, Methods and Algorithms, Artificial Intelligence, Autonomous Ships, Navigation, Manoeuvering and Ship-handling Simulation, Marine Traffic Control and Automatic Identification Systems}
}