To alleviate the pressure on crew members in the lookout and warning tasks during ship navigation, this research developed a ship electronic lookout (SEL) using the Low-Rank Adaption (LoRA) fine-tuned Large Language Model (LLM). The SEL can synthesize heterogeneous information from multiple sensors, such as CCTV and AIS, to depict the current navigation environment in real-time and provide lookout alerts. Specifically, real-world navigation data is first collected, and the sensor information is processed into a dataset (LookOut). Subsequently, Qwen2-7B-Instruct was selected as the baseline to balance model responsiveness and safety. Finally, LoRA was used for supervised fine-tuning (SFT) to develop the SEL. Experimental demonstrate that LLM fine-tuned for specific application scenarios can mimic the language norms of the ship lookout and be combined with perception systems to achieve electronic lookout.


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    Titel :

    Developing Ship Electronic Lookout Using LoRA Fine-Tuned Large Language Model


    Weitere Titelangaben:

    Lect. Notes Electrical Eng.


    Beteiligte:
    Jia, Limin (Herausgeber:in) / Ou, Dongxiu (Herausgeber:in) / Liu, Hui (Herausgeber:in) / Zong, Fang (Herausgeber:in) / Wang, Pangwei (Herausgeber:in) / Zhang, Mingfang (Herausgeber:in) / Ma, Feng (Autor:in) / Wang, Xiumin (Autor:in) / Lv, Weiqian (Autor:in) / Guo, Hua (Autor:in)

    Kongress:

    International Conference on Artificial Intelligence and Autonomous Transportation ; 2024 ; Beijing, China December 06, 2024 - December 08, 2024



    Erscheinungsdatum :

    31.03.2025


    Format / Umfang :

    8 pages





    Medientyp :

    Aufsatz/Kapitel (Buch)


    Format :

    Elektronische Ressource


    Sprache :

    Englisch




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