Incorporating Probabilistic Mutual Interactions in Simulation-Based Safety Evaluation of Maritime Autonomous Surface Ships

This paper proposes a “Probabilistic 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 “shift 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.