Prediction-enabled path planning for multi-ship encounters in Oslofjord
Mingda Zhu, W. H. Tian, Robert Skulstad, Houxiang Zhang, Guoyuan Li
Abstract
Maritime navigation in multi-ship encounters presents challenges due to dynamic perturbations and complexity, which lead to elevated safety concerns. Conventional path-planning methods exhibit several issues. This paper addresses these issues by proposing a prediction-enabled path-planning method for multi-ship encounters in the Horten and Moss area within Oslofjord, Norway, the first industrial testbed for autonomous ferries nationwide. The method leverages action classification and trajectory projection techniques to enhance path planning with a more informed decision-making process. A convolution neural network is employed for the binary classification of the own ship’s action classes, and a dynamic time-warping approach is applied to predict the future positions of the target ships based on trajectory similarity. Experiments utilizing Automatic Identification System data from 2013 to 2019 demonstrated the method’s effectiveness. Classification results show a satisfactory F1 score, and comparative evaluations with the classic A* method showcase superior performance. Real-case tests affirm the method’s ability to provide a trackable path while ensuring a safe distance between the own ship and target ships.