面向智能交通系统的大型多模态智能体：架构、证据与部署挑战
Large Multimodal Agents for Intelligent Transportation Systems: Architectures, Evidence, and Deployment Challenges
AI总结：
本综述梳理2023-2026年42个LMAs研究家族，评估其在ITS中的多模态性、性能与部署情况，支持有限编排而非替代，提供评估协议与部署路线图。
AI中文摘要：
大型多模态智能体（LMAs）正被越来越多地应用于智能交通系统（ITS），但现有研究常将多模态性、智能体属性、实证性能与部署就绪度混为一谈。本综述基于91个已映射来源，提供了2023年1月至2026年8月3日期间发布的42个主要研究家族的可审计证据图谱，区分了模型级、系统级与混合多模态性，并按系统架构和行动权限对每个家族进行分类。证据通过功能能力（C0-C3）、验证设置（E0-E4）、三个证据命题（P1-P3）及八个方法学关注领域（Q1-Q8）独立评估：23个家族直接评估交通语义（P1），24个评估多维度整合（P3），19个同时评估两者；证据协调（P2）仍未解决，无家族展示完整的溯源-挑战处理-比较-结果链条。14个家族达到C3，但13个仍处于E2，仅1个达到E3，无达到E4的。在ITS领域，LMAs在语义解释、意图转换、证据组织、场景创作、解释及专业工具协调方面获得最佳支持；数值预测、优化、仿真保真度、硬约束、低级控制、安全回退及最终权限应保留给可独立验证的专业系统或负责任的人类。因此，本综述支持有限编排而非替代，并提供了匹配的比较评估协议与负责任部署的阶段性路线图，该动态证据库可在指定URL获取。
英文摘要：
Large multimodal agents (LMAs) are increasingly proposed for intelligent transportation systems (ITS), but existing studies often conflate multimodality, agency, empirical performance, and deployment readiness. This review provides an auditable evidence map of 42 primary study families released between January 2023 and 3 August 2026 within a corpus of 91 mapped sources. It distinguishes model-level, system-level, and hybrid multimodality and classifies each family by system architecture and action authority. Evidence is assessed independently through functional capability (C0-C3), validation setting (E0-E4), three evidence propositions (P1-P3), and eight methodological-concern domains (Q1-Q8). Transportation semantics (P1) are directly evaluated in 23 families and multidimensional integration (P3) in 24; 19 families directly evaluate both. Evidence reconciliation (P2) remains unresolved because no family demonstrates the complete provenance-challenge-handling-comparison-outcome chain. Fourteen families reach C3, but 13 remain at E2; only one reaches E3 and none reaches E4. Across ITS domains, LMAs are best supported for semantic interpretation, intent translation, evidence organisation, scenario authoring, explanation, and specialist-tool coordination. Numerical forecasting, optimisation, simulation fidelity, hard constraints, low-level control, safety fallback, and final authority should remain with independently verifiable specialist systems or accountable humans. The review therefore supports bounded orchestration rather than replacement and provides a matched comparative evaluation protocol and staged roadmap for accountable deployment. The living evidence repository is available at https://github.com/pangjunbiao/ITS-LMA-Review.
