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Research on ship trajectory prediction and behavior recognition based on multi-task Informer model
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Shigang LI1, 2, Kezhong LIU1, 3, 4, Lijia CHEN1, 3, 4, Naiqi ZHOU1, 3, 4, Yang ZHOU1, 3, 4, Jiatao HUANG1, 3, 4
Navigation of China | 2025, 48(3) : 157 - 165
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Navigation of China | 2025, 48(3): 157-165
• Intelligent Shipping •
Research on ship trajectory prediction and behavior recognition based on multi-task Informer model
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Shigang LI1, 2, Kezhong LIU1, 3, 4, Lijia CHEN1, 3, 4, Naiqi ZHOU1, 3, 4, Yang ZHOU1, 3, 4, Jiatao HUANG1, 3, 4
Affiliations
  • 1.School of Navigation, Wuhan University of Technology, Wuhan 430063, China
  • 2.Eastern Navigation Service Center, Maritime Safety Administration, People’s Republic of China, Shanghai 200086, China
  • 3.Hubei Key Laboratory of Inland Shipping Technology, Wuhan University of Technology, Wuhan 430063, China
  • 4.National Key Laboratory of Waterway Traffic Control, Wuhan University of Technology, Wuhan 430063, China
Published: 2025-09-25 doi: 10.3969/j.issn.1000-4653.2025.03.019
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Ship trajectory prediction and behavior recognition can help effectively assess navigational risks and provide an important basis for decision-making in collision avoidance and traffic management. To improve the accuracy of ship trajectory prediction and behavior recognition, this paper studies a multi-task Informer model for simultaneous trajectory prediction and behavior recognition. Based on the Informer framework, the model incorporates a multi-task learning approach. It addresses the issue that inaccurate ship behavior records in AIS data cannot be directly used as model inputs by designing a multi-task loss function that jointly trains behavior recognition and trajectory prediction in parallel. During training, an adaptive updating strategy for the loss function-based on homoscedastic uncertainty-is designed to automatically allocate weights to the losses of the two tasks. Evaluated using real AIS data from the Taicang sector waters, the multi-task Informer model reduces trajectory prediction loss by 40.2% and 14.7% compared to LSTM and Informer models, respectively. In behavior recognition, the multi-task model improves accuracy by 11.7% and 5.95% compared to LSTM and Informer models, respectively. The results demonstrate that the multi-task model effectively enhances the performance of ship trajectory prediction while achieving accurate recognition of ship behavior.

trajectory prediction  /  behavioral discrimination  /  AIS data  /  Informer  /  Multi task learning
Shigang LI, Kezhong LIU, Lijia CHEN, Naiqi ZHOU, Yang ZHOU, Jiatao HUANG. Research on ship trajectory prediction and behavior recognition based on multi-task Informer model[J]. Navigation of China, 2025 , 48 (3) : 157 -165 . DOI: 10.3969/j.issn.1000-4653.2025.03.019
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Year 2025 volume 48 Issue 3
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Article Info
doi: 10.3969/j.issn.1000-4653.2025.03.019
  • Receive Date：2024-11-19
  • Online Date：2026-03-17
  • Published：2025-09-25
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  • Received：2024-11-19
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Affiliations
    1.School of Navigation, Wuhan University of Technology, Wuhan 430063, China
    2.Eastern Navigation Service Center, Maritime Safety Administration, People’s Republic of China, Shanghai 200086, China
    3.Hubei Key Laboratory of Inland Shipping Technology, Wuhan University of Technology, Wuhan 430063, China
    4.National Key Laboratory of Waterway Traffic Control, Wuhan University of Technology, Wuhan 430063, China
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表12种不同金属材料的力学参数
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Family		 属数
Number of
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Number of
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鹅膏菌科Amanitaceae 2 11 5.26 鹅膏菌属 Amanita 10 4.78
小菇科 Mycenaceae 2 12 5.74 丝盖伞属 Inocybe 5 2.39
多孔菌科 Polyporaceae 8 14 6.70 蜡蘑属 Laccaria 5 2.39
红菇科 Russulaceae 3 23 11.00 小皮伞属 Marasmius 6 2.87
小菇属 Mycena 11 5.26
光柄菇属 Pluteus 5 2.39
红菇属 Russula 17 8.13
栓菌属 Trametes 5 2.39
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