Collision Avoidance Decision-Making Method for Unmanned Surface Vehicles Considering Uncertain Navigation Intent of Target Ships

In complex and dynamic maritime environments, the uncertain navigation intent of target ships (TS) poses a significant challenge for the collision avoidance strategies of unmanned surface vehicles (USVs). To address above issue, an autonomous collision avoidance method that integrates recursive Bayesian estimation, a masked attention mechanism, and the soft actor-critic (SAC) algorithm is proposed in this paper. Specifically, the recursive Bayesian model is employed to estimate the navigation intentions of TS in real time, for enabling the construction of intent-aware state representations that enhance the modeling capability for uncertain target behaviors. In addition, a maximum target count is predefined, and the masked attention mechanism is incorporated into the actor and critic networks of SAC to resolve the mismatch between the fixed input dimensionality of neural networks and the dynamically varying number of TS. Simulation demonstrates that the proposed method outperforms traditional collision avoidance methods and other state-of-the-art deep reinforcement learning (DRL)-based strategies in terms of key performance metrics such as collision avoidance success rate, minimum distance, and average risk. Furthermore, real-field experiments validate the proposed method’s adaptability to real-world maritime environments and its potential for practical deployment.