Large Multimodal Agents for Intelligent Transportation Systems: Architectures, Evidence, and Deployment Challenges
Muhammad Ayub Sabir, Shaohong Zheng, Zhiyu Qu, Fatima Ashraf, Junbiao Pang
Why It Matters
What makes this one worth your time
This review synthesizes current research on multimodal agents, offering insights that can guide future developments and implementations in intelligent transportation systems.
A comprehensive review of large multimodal agents in intelligent transportation systems, highlighting their capabilities and deployment challenges.
Summary
The paper reviews 42 families of large multimodal agents for intelligent transportation systems, assessing their architectures, empirical performance, and deployment challenges, while providing a roadmap for accountable deployment.
Key contributions
- An auditable evidence map of multimodal agents in ITS.
- Classification of study families by system architecture and action authority.
- A matched comparative evaluation protocol and staged roadmap for deployment.
Notable insights
- The distinction between model-level, system-level, and hybrid multimodality provides a nuanced understanding of agent capabilities.
- The unresolved issue of evidence reconciliation indicates significant gaps in current research methodologies.
Possible limitations
- Not stated in the abstract.
Abstract
arXiv:2608.08184v1 Announce Type: new Abstract: 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.