将大型语言模型集成到交通系统中:集成水平,能力边界和信息理论视角
Wenwen Tu1, Junfan Li2, Feng Xiao3
1Engineering Training Center, Kunming University of Science and Technology, Kunming 650500, China.
Entropy (Basel, Switzerland)
|February 27, 2026
概括
大型语言模型 (LLM) 通过处理复杂的数据,彻底改变了智能交通系统. 然而,它们的离散推理与连续流量环境发生冲突,未来系统需要混合智能.
科学领域:
- 智能运输系统 智能运输系统
- 人工智能的人工智能
- 信息理论 信息理论
背景情况:
- 大型语言模型 (LLM) 为智能交通系统提供了语义抽象和多模式融合等高级功能.
- 关于LLM集成的现有研究涵盖了对自主代理人的数据表示.
研究的目的:
- 通过信息理论的镜头来审查LLM在智能交通系统的整合.
- 在连续,安全关键的交通环境中分析LLM的局限性.
- 为下一代交通系统提出混合智能架构.
主要方法:
- 信息理论分析将LLMs概念化为减少的系统.
- 对LLM整合模式和局限性的检查.
- 划出LLM能力与经典模型之间的界限.
主要成果:
- 法学士擅长管理高语义 (上下文理解,知识整合).
- 在LLM的离散推理和交通系统的连续性,因果性之间存在着根本的不匹配.
- 基于经典物理和优化模型对于低的领域 (实时控制,安全验证) 至关重要.
结论:
- 混合智能架构对于下一代交通系统至关重要.
- 这些架构必须将基于LLM的语义处理与物理系统建模相结合.
- 对于语义任务来说,LLM至关重要,而经典模型则确保物理和因果完整性.
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