In recent years, with the continuous development of the global economy, maritime transportation has become an essential mode of transportation for both domestic and international trade, making it an urgent task for countries around the world to improve the intelligence level of maritime traffic management. As a key technology in real-time vessel monitoring, traffic hazard early warning, and traffic flow estimation, vessel trajectory prediction has been widely applied across both civil and commercial sectors. To address the problems with existing trajectory prediction methods, such as difficulty in establishing real-time vessel kinematic models, limited understanding of vessel navigation strategies, and poor adaptability for long-term prediction, this paper proposes a deep reinforcement learning-based vessel trajectory prediction method. Firstly, the trajectory prediction problem is formulated as a Markov Decision Process (MDP), thereby transforming the prediction problem into an optimal policy-solving problem of the MDP. Secondly, given the complexity of the trajectory prediction problem, a convolutional neural network (CNN) is adopted to parameterize the policy network, and a CNN-PPO deep reinforcement learning method is used to learn the target vessel’s navigation strategy from historical trajectories. Comparative experimental results show that the proposed algorithm is not only suitable for predicting the target’s position at a specific future moment but also offers clear advantages in trajectory prediction across multiple future moments.
Zheng et al. (Mon,) studied this question.