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<p>As intelligent cockpits rapidly evolve towards “proactive natural interaction,” traditional rule-based user behavior inference methods are facing scalability, generalization, and accuracy bottlenecks, leading to the development and deployment of functions oriented towards pseudo-demands. Effectively capturing and representing the hidden associative knowledge in intelligent cockpits can enhance the system’s understanding of user behavior and environmental contexts, thereby precisely discerning real user needs. In this context, knowledge graphs (KGs) have emerged as an effective tool, enabling the retrieval and organization of vast amounts of information within interconnected and interpretable structures. However, rapidly and flexibly generating domain-specific KGs still poses significant challenges. To address this, this paper introduces a novel knowledge graph construction (KGC) model, GLM-TripleGen, dedicated to analyzing the states and behaviors within intelligent cockpits. This model aims to precisely mine the latent relationships between cockpit state factors and behavioral sequences, effectively addressing key challenges such as the ambiguity in entity recognition and the complexity of relationship extraction within cockpit data. To enhance the adaptability of GLM-TripleGen to the intelligent cockpit domain, this paper constructs an instruction-following dataset based on vehicle states and in-cockpit interaction behaviors, containing a large number of prompt texts paired with corresponding triple labels, to support model fine-tuning. During the fine-tuning process, the Low-Rank Adaptation (LoRA) method is employed to effectively optimize model parameters, significantly reducing training costs. Extensive experiments demonstrate that GLM-TripleGen outperforms existing state-of-the-art KGC methods, accurately generating normalized cockpit triple units. Furthermore, GLM-TripleGen exhibits exceptional robustness and generalization ability, handling various unknown entities and relationships with minimal generalization processing.</p>

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Knowledge graph construction for intelligent cockpits based on large language models

  • 期刊论文

  • 出版模式：

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  • 发表日期：

    2025年3月4日

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  • Haomin Dong,
  • Wenbin Wang,
  • Zhenjiang Sun,
  • Ziyi Kang,
  • Xiaojun Ge,
  • Fei Gao,
  • Jixin Wang

摘要

As intelligent cockpits rapidly evolve towards “proactive natural interaction,” traditional rule-based user behavior inference methods are facing scalability, generalization, and accuracy bottlenecks, leading to the development and deployment of functions oriented towards pseudo-demands. Effectively capturing and representing the hidden associative knowledge in intelligent cockpits can enhance the system’s understanding of user behavior and environmental contexts, thereby precisely discerning real user needs. In this context, knowledge graphs (KGs) have emerged as an effective tool, enabling the retrieval and organization of vast amounts of information within interconnected and interpretable structures. However, rapidly and flexibly generating domain-specific KGs still poses significant challenges. To address this, this paper introduces a novel knowledge graph construction (KGC) model, GLM-TripleGen, dedicated to analyzing the states and behaviors within intelligent cockpits. This model aims to precisely mine the latent relationships between cockpit state factors and behavioral sequences, effectively addressing key challenges such as the ambiguity in entity recognition and the complexity of relationship extraction within cockpit data. To enhance the adaptability of GLM-TripleGen to the intelligent cockpit domain, this paper constructs an instruction-following dataset based on vehicle states and in-cockpit interaction behaviors, containing a large number of prompt texts paired with corresponding triple labels, to support model fine-tuning. During the fine-tuning process, the Low-Rank Adaptation (LoRA) method is employed to effectively optimize model parameters, significantly reducing training costs. Extensive experiments demonstrate that GLM-TripleGen outperforms existing state-of-the-art KGC methods, accurately generating normalized cockpit triple units. Furthermore, GLM-TripleGen exhibits exceptional robustness and generalization ability, handling various unknown entities and relationships with minimal generalization processing.