Real-time Collision Avoidance Considering Ship Position Uncertainty Based on AIS Data

Autonomous ships require reliable situational awareness to ensure safe navigation, particularly during collision avoidance in congested maritime environments. Although an automatic identification system (AIS) is widely used for vessel traffic awareness, its update intervals, communication delays, and measurement noise introduce positional uncertainty, which limits its direct application to real-time collision avoidance. This study aims to develop a collision avoidance framework that incorporates AIS-derived position uncertainty from the perspective of an autonomous ship. An unscented Kalman filter is applied to estimate the nonlinear motion and positional uncertainty of surrounding vessels in real time to compensate for information gaps in AIS data. Using the predicted vessel states, collision risk is quantitatively evaluated through a collision risk index, and avoidance maneuvers are generated using the velocity obstacle method. The framework is validated through simulations in which positional uncertainty is introduced deliberately, and vessel maneuvering behavior is represented using a dynamic model derived from the maneuvering modeling group theory. Simulation results show that the proposed system improves the consistency of collision risk assessment and estimates target vessel trajectories with an average positional accuracy of ~3 m. This corresponds to an ~96% reduction in estimation error compared to that obtained using nonpredictive approaches.