系统梳理42项交通智能体研究，提出可验证的评估框架与部署路线。
Large Multimodal Agents for Intelligent Transportation Systems: Architectures, Evidence, and Deployment Challenges
- 区分模型、系统与混合多模态，按架构与权限分类研究家族。
- 仅14个达到最高功能能力，13个仅达中等验证级别，证据链不完整。
- 主张有限协同而非替代，适合关注可问责部署的研究者与工程师。
大型多模态智能体（LMAs）在智能交通系统（ITS）中的应用日益增多，但现有研究常混淆多模态性、自主性、实证表现与部署成熟度。本综述基于2023年1月至2026年8月3日发布的91篇文献，梳理了42个主要研究家族。通过功能能力（C0-C3）、验证场景（E0-E4）、三项证据命题（P1-P3）及八项方法论关切领域（Q1-Q8）独立评估证据。23个家族直接评估交通语义（P1），24个评估多维融合（P3），其中19个同时评估两者。证据一致性（P2）未被解决——无一家族完整呈现来源-挑战应对-对比-结果链条。14个达C3级功能能力，但13个仍处E2验证阶段；仅1个达E3，无一达E4。LMAs在语义解析、意图转换、证据组织、场景构建、解释生成与专家工具协调方面支持最佳；数值预测、优化、仿真保真度、硬约束处理、底层控制、安全冗余与最终决策权应由独立可验证的专用系统或人类负责。因此，建议采用受限协同模式而非完全替代，并提供匹配的比较评估协议与分阶段可问责部署路线。动态证据库见https://github.com/pangjunbiao/ITS-LMA-Review。
原文摘要 · Abstract (English)
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.
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