AI交通科学家：大模型驱动的自主交通科研

宫晓燕; 戴星原; 李芮霖; 吕宜生

doi:10.11959/j.issn.2096-6652.202605

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AI交通科学家：大模型驱动的自主交通科研

更新时间：2026-03-23

- AI交通科学家：大模型驱动的自主交通科研
- AI transportation scientist: LLM-driven autonomous research
- 智能科学与技术学报 2026年页码：1-14
- 作者机构：
  
  1.中国科学院自动化研究所，北京 100190
  2.道路交通安全管控技术国家工程研究中心，北京 100006
  3.山东交通学院，济南 250357
  4.澳门科技大学创新工程学院，澳门 999078
- 作者简介：
  
  [ "宫晓燕（1976- ），女，博士，中国科学院自动化研究所多模态人工智能系统全国重点实验室高级工程师，主要研究方向为复杂系统管理与控制、交通流预测、人工智能。" ]
  [ "戴星原（1993- ），男，博士，中国科学院自动化研究所多模态人工智能系统全国重点实验室副研究员，主要研究方向为平行交通、平行驾驶、强化学习。" ]
  [ "李芮霖（2002- ），女，山东交通学院硕士生，主要研究方向为交通信号管控及交通管理。" ]
  [ "吕宜生（1983- ），男，博士，中国科学院自动化研究所多模态人工智能系统全国重点实验室研究员，澳门科技大学创新工程学院教授，主要研究方向为智能交通、无人驾驶、人工智能。" ]
- 基金信息：
  
  国家自然科学基金项目(62271485;62303462);道路交通安全管控技术国家工程研究中心开放课题(2024GCZXKFKT11A);山东省交通运输厅科技计划项目(2024B70);山东高速集团有限公司科技计划项目(HS2023B044)
- DOI：10.11959/j.issn.2096-6652.202605
  中图分类号： TP39
- 收稿：2025-12-16，
  
  修回：2026-03-13，
  
  录用：2026-03-17，
  
  网络首发：2026-03-23，
- 稿件说明：
移动端阅览
宫晓燕,戴星原,李芮霖等.AI交通科学家：大模型驱动的自主交通科研[J].智能科学与技术学报,

Gong Xiaoyan,Dai Xingyuan,Li Ruilin,et al.AI transportation scientist: LLM-driven autonomous research[J].Chinese Journal of Intelligent Science and Technology,
宫晓燕,戴星原,李芮霖等.AI交通科学家：大模型驱动的自主交通科研[J].智能科学与技术学报, DOI：10.11959/j.issn.2096-6652.202605.

Gong Xiaoyan,Dai Xingyuan,Li Ruilin,et al.AI transportation scientist: LLM-driven autonomous research[J].Chinese Journal of Intelligent Science and Technology, DOI：10.11959/j.issn.2096-6652.202605.

摘要

城市交通系统正加速演化为信息-物理-社会系统（cyber-physical-social system，CPSS），无人车、无人机及各类智能体持续融入，导致系统复杂性、动态性与耦合性急剧增强。传统以人工为主的“作坊式”科研模式，难以实现对系统快速演化行为的深入解析与实时响应。为应对上述挑战，立足平行智能理论，提出融合大模型认知能力与多智能体协同优势的自主交通科研框架“AI交通科学家”。该框架构建交互层、认知层、实验层、支撑层4层架构，并以动态路由为调度引擎，针对机理发现、策略验证与系统优化3类交通科研问题，通过动态路由自适应编排各层智能体协作链路，形成匹配不同任务需求的科研流程。框架贯通文献分析、问题识别、假设生成、仿真验证、策略生成、反馈优化的全链条人机协同闭环，不仅能实现复杂交通问题的自动化识别与高效求解，还能驱动交通系统规律的自主挖掘，以及管控策略的创新迭代、多场景验证与持续优化，为CPSS背景下交通科研的智能化转型提供可落地的技术范式。

Abstract

Urban transportation systems are rapidly evolving into CPSS (cyber-physical-social System)

driven by the continuous integration of autonomous vehicles

unmanned aerial vehicles

and diverse intelligent agents. This evolution has dramatically increased system complexity

dynamics

and coupling

rendering traditional human-centric research paradigms insufficient for timely understanding and response to fast-evolving system behaviors. To address these challenges

an autonomous framework called "AI Transportation Scientist" was proposed to revolutionize transportation research through parallel intelligence. The architecture leveraged a synergy between large language model and multi-agent systems across four functional layers (interaction

cognitive

experimental

and support). At its core

a dynamic routing engine adaptively scheduled intelligent agents to tackle mechanism discovery

strategy validation

and system optimization. By implementing a full-chain collaborative closed loop—encompassing problem identification

simulation

and feedback optimization—the framework enabled the autonomous discovery of transportation laws and the continuous evolution of control strategies. This research establishes a scalable technical paradigm for advancing transportation science within CPSS environments

ensuring both efficient problem-solving and innovative strategy iteration.

关键词

Keywords

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