AI transportation scientist: LLM-driven autonomous transportation research

Gong Xiaoyan; Dai Xingyuan; Li Ruilin; Lyu Yisheng

doi:10.11959/j.issn.2096-6652.202605

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AI transportation scientist: LLM-driven autonomous transportation research

Papers and Reports | 更新时间：2026-04-27

- AI transportation scientist: LLM-driven autonomous transportation research
- Chinese Journal of Intelligent Science and Technology Vol. 8, Issue 1, Pages: 33-46(2026)
- 作者机构：
  
  1.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，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，山东济南 250357
  4.澳门科技大学创新工程学院，澳门 999078
- 作者简介：
- 基金信息：
  
  The National Natural Science Foundation of China(62271485;62303462);Open Project of the National Engineering Research Center for Road Traffic Safety Management Technology(2024GCZXKFKT11A);The Science and Technology Project of Shandong Provincial Department of Transportation(2024B70);SDHS Science and Technology Project(HS2023B044)
- DOI：10.11959/j.issn.2096-6652.202605
  CLC： TP39
- Received：16 December 2025，
  
  Revised：2026-03-13，
  
  Accepted：17 March 2026，
  
  Published：15 March 2026
- 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,2026,08(01):33-46.

Gong Xiaoyan,Dai Xingyuan,Li Ruilin,et al.AI transportation scientist: LLM-driven autonomous transportation research[J].Chinese Journal of Intelligent Science and Technology,2026,08(01):33-46.
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,2026,08(01):33-46. DOI： 10.11959/j.issn.2096-6652.202605.

Gong Xiaoyan,Dai Xingyuan,Li Ruilin,et al.AI transportation scientist: LLM-driven autonomous transportation research[J].Chinese Journal of Intelligent Science and Technology,2026,08(01):33-46. DOI： 10.11959/j.issn.2096-6652.202605.

摘要

城市交通系统正加速演化为社会物理信息系统（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 system 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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