Large Multimodal Agents for Intelligent Transportation Systems: Architectures, Evidence, and Deployment Challenges | Paper Citation Record

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This paper cites The PRISMA 2020 Statement: An Updated Guideline for Reporting Systematic Reviews,.

Large Multimodal Agents for Intelligent Transportation Systems: Architectures, Evidence, and Deployment Challenges The PRISMA 2020 Statement: An Updated Guideline for Reporting Systematic Reviews,

Reference 1

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This paper cites Taxonomy and Definitions for Terms Re- lated to Driving Automation Systems for On-Road Motor Vehicles,.

Large Multimodal Agents for Intelligent Transportation Systems: Architectures, Evidence, and Deployment Challenges Taxonomy and Definitions for Terms Re- lated to Driving Automation Systems for On-Road Motor Vehicles,

Reference 2

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This paper cites TRIP: Transport Reasoning With Intelligence Progression—A Foundation Framework,.

Large Multimodal Agents for Intelligent Transportation Systems: Architectures, Evidence, and Deployment Challenges TRIP: Transport Reasoning With Intelligence Progression—A Foundation Framework,

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This paper cites Large multimodal agents: A survey,.

Large Multimodal Agents for Intelligent Transportation Systems: Architectures, Evidence, and Deployment Challenges Large multimodal agents: A survey,

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This paper cites A Survey on Multimodal Large Language Models for Autonomous Driving,.

Large Multimodal Agents for Intelligent Transportation Systems: Architectures, Evidence, and Deployment Challenges A Survey on Multimodal Large Language Models for Autonomous Driving,

Reference 5

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This paper cites Large Language Models for Intelligent Transportation: A Review of the State of the Art and Challenges,.

Large Multimodal Agents for Intelligent Transportation Systems: Architectures, Evidence, and Deployment Challenges Large Language Models for Intelligent Transportation: A Review of the State of the Art and Challenges,

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Observation 96a44c96-94d3-4882-902c-49c7cc4ce7e9 · outbound

This paper cites Vision language models in autonomous driving: A survey and outlook,.

Large Multimodal Agents for Intelligent Transportation Systems: Architectures, Evidence, and Deployment Challenges Vision language models in autonomous driving: A survey and outlook,

Reference 8

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This paper cites Exploring the Roles of Large Lan- guage Models in Reshaping Transportation Systems: A Survey, Framework,andRoadmap,.

Large Multimodal Agents for Intelligent Transportation Systems: Architectures, Evidence, and Deployment Challenges Exploring the Roles of Large Lan- guage Models in Reshaping Transportation Systems: A Survey, Framework,andRoadmap,

Reference 9

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This paper cites Ap- plications of Large Language Models and Generative AI in Transportation:ASystematicReviewandBibliometricAnalysis,.

Large Multimodal Agents for Intelligent Transportation Systems: Architectures, Evidence, and Deployment Challenges Ap- plications of Large Language Models and Generative AI in Transportation:ASystematicReviewandBibliometricAnalysis,

Reference 10

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This paper cites Large language models for transportation research: Methodologies, state of the art, and future opportu- nities,.

Large Multimodal Agents for Intelligent Transportation Systems: Architectures, Evidence, and Deployment Challenges Large language models for transportation research: Methodologies, state of the art, and future opportu- nities,

Reference 11

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This paper cites A survey of large language models in transportation planning: Modelling, design and decision-making,.

Large Multimodal Agents for Intelligent Transportation Systems: Architectures, Evidence, and Deployment Challenges A survey of large language models in transportation planning: Modelling, design and decision-making,

Reference 12

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This paper cites Harnessing large language models for intel- ligent transportation systems: A systematic review,.

Large Multimodal Agents for Intelligent Transportation Systems: Architectures, Evidence, and Deployment Challenges Harnessing large language models for intel- ligent transportation systems: A systematic review,

Reference 13

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This paper cites UrbanGPT: Spatio-Temporal Large Language Models,.

Large Multimodal Agents for Intelligent Transportation Systems: Architectures, Evidence, and Deployment Challenges UrbanGPT: Spatio-Temporal Large Language Models,

Reference 14

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This paper cites TSGDiff: Traffic State Gen- erative Diffusion Model Using Multi-Source Information Fusion,.

Large Multimodal Agents for Intelligent Transportation Systems: Architectures, Evidence, and Deployment Challenges TSGDiff: Traffic State Gen- erative Diffusion Model Using Multi-Source Information Fusion,

Reference 15

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This paper cites A Heterogeneous Graph Convolution Based Method for Short-Term OD Flow Completion and Prediction in a Metro System,.

Large Multimodal Agents for Intelligent Transportation Systems: Architectures, Evidence, and Deployment Challenges A Heterogeneous Graph Convolution Based Method for Short-Term OD Flow Completion and Prediction in a Metro System,

Reference 16

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This paper cites DriveLM: Driving with Graph Visual Question Answering,.

Large Multimodal Agents for Intelligent Transportation Systems: Architectures, Evidence, and Deployment Challenges DriveLM: Driving with Graph Visual Question Answering,

Reference 17

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This paper cites A Language Agent for Autonomous Driving,.

Large Multimodal Agents for Intelligent Transportation Systems: Architectures, Evidence, and Deployment Challenges A Language Agent for Autonomous Driving,

Reference 18

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This paper cites VLM-RL: A unified vision language models and reinforcement learning frame- work for safe autonomous driving,.

Large Multimodal Agents for Intelligent Transportation Systems: Architectures, Evidence, and Deployment Challenges VLM-RL: A unified vision language models and reinforcement learning frame- work for safe autonomous driving,

Reference 19

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Observation 43f5d23f-b920-483b-bf82-b8a3af6db7c6 · outbound

This paper cites OmniDrive: A Holistic Vision-Language Dataset for Autonomous Driving with Counterfactual Reason- ing,.

Large Multimodal Agents for Intelligent Transportation Systems: Architectures, Evidence, and Deployment Challenges OmniDrive: A Holistic Vision-Language Dataset for Autonomous Driving with Counterfactual Reason- ing,

Reference 20

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Observation 985efcb3-e5d0-4eaf-a2c4-4eddcbbaa3f2 · outbound

This paper cites RACP: Risk-aware contingency planning with multi-modal predictions,.

Large Multimodal Agents for Intelligent Transportation Systems: Architectures, Evidence, and Deployment Challenges RACP: Risk-aware contingency planning with multi-modal predictions,

Reference 21

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This paper cites LMDrive: Closed-Loop End-to-End Driving with Large Language Models,.

Large Multimodal Agents for Intelligent Transportation Systems: Architectures, Evidence, and Deployment Challenges LMDrive: Closed-Loop End-to-End Driving with Large Language Models,

Reference 22

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Observation 8dd6bebb-b0b9-43aa-8cfc-d64f439e017a · outbound

This paper cites LLMLight: Large Language Models as Traffic Signal Control Agents,.

Large Multimodal Agents for Intelligent Transportation Systems: Architectures, Evidence, and Deployment Challenges LLMLight: Large Language Models as Traffic Signal Control Agents,

Reference 23

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This paper cites The Crossroads of LLM and Traffic Control: A Study on Large Language Models in Adaptive Traffic Signal Control,.

Large Multimodal Agents for Intelligent Transportation Systems: Architectures, Evidence, and Deployment Challenges The Crossroads of LLM and Traffic Control: A Study on Large Language Models in Adaptive Traffic Signal Control,

Reference 24

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This paper cites Large Language Models as Traffic Control Systems at Urban Intersections: A New Paradigm,.

Large Multimodal Agents for Intelligent Transportation Systems: Architectures, Evidence, and Deployment Challenges Large Language Models as Traffic Control Systems at Urban Intersections: A New Paradigm,

Reference 25

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This paper cites PressLight: Learning Max Pressure Control to Coordinate Traffic Signals in Arterial Network,.

Large Multimodal Agents for Intelligent Transportation Systems: Architectures, Evidence, and Deployment Challenges PressLight: Learning Max Pressure Control to Coordinate Traffic Signals in Arterial Network,

Reference 26

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This paper cites Traffic Light Optimization With Low Penetra- tion Rate Vehicle Trajectory Data,.

Large Multimodal Agents for Intelligent Transportation Systems: Architectures, Evidence, and Deployment Challenges Traffic Light Optimization With Low Penetra- tion Rate Vehicle Trajectory Data,

Reference 27

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Observation 85e8c817-874f-4e77-8403-5e30c30faa73 · outbound

This paper cites Using Multimodal Large Language Models for Automated Detection of Traffic Safety-Critical Events,.

Large Multimodal Agents for Intelligent Transportation Systems: Architectures, Evidence, and Deployment Challenges Using Multimodal Large Language Models for Automated Detection of Traffic Safety-Critical Events,

Reference 28

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This paper cites VRU-Accident: A vision–language benchmark for video question answering and dense captioning for accident scene understanding,.

Large Multimodal Agents for Intelligent Transportation Systems: Architectures, Evidence, and Deployment Challenges VRU-Accident: A vision–language benchmark for video question answering and dense captioning for accident scene understanding,

Reference 29

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This paper cites SafePLUG: Empowering multimodal LLMs with pixel-level insight and temporal grounding for traffic accident understanding,.

Large Multimodal Agents for Intelligent Transportation Systems: Architectures, Evidence, and Deployment Challenges SafePLUG: Empowering multimodal LLMs with pixel-level insight and temporal grounding for traffic accident understanding,

Reference 30

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This paper cites Large Language Models in Analyzing Crash Narratives -- A Comparative Study of ChatGPT, BARD and GPT-4.

Large Multimodal Agents for Intelligent Transportation Systems: Architectures, Evidence, and Deployment Challenges Large Language Models in Analyzing Crash Narratives -- A Comparative Study of ChatGPT, BARD and GPT-4

Reference 31

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This paper cites Agentic Large Language Models for Day- to-Day Route Choices,.

Large Multimodal Agents for Intelligent Transportation Systems: Architectures, Evidence, and Deployment Challenges Agentic Large Language Models for Day- to-Day Route Choices,

Reference 32

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This paper cites TransitGPT: A Generative AI- Based Framework for Interacting with GTFS Data Using Large Language Models,.

Large Multimodal Agents for Intelligent Transportation Systems: Architectures, Evidence, and Deployment Challenges TransitGPT: A Generative AI- Based Framework for Interacting with GTFS Data Using Large Language Models,

Reference 33

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This paper cites ChatSUMO: Large Language Model for Automating Traffic Scenario Generation in Simulation of Urban MObility,.

Large Multimodal Agents for Intelligent Transportation Systems: Architectures, Evidence, and Deployment Challenges ChatSUMO: Large Language Model for Automating Traffic Scenario Generation in Simulation of Urban MObility,

Reference 34

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This paper cites Speak to Simulate: An LLM-Guided Agentic Framework for Traffic Simulation in SUMO,.

Large Multimodal Agents for Intelligent Transportation Systems: Architectures, Evidence, and Deployment Challenges Speak to Simulate: An LLM-Guided Agentic Framework for Traffic Simulation in SUMO,

Reference 35

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Large Multimodal Agents for Intelligent Transportation Systems: Architectures, Evidence, and Deployment Challenges Automating Traffic Model Enhancement With AI Research Agent,

Reference 36

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This paper cites Not What You’ve Signed Up For: Compromising Real-World LLM-Integrated Applications with Indirect Prompt Injection,.

Large Multimodal Agents for Intelligent Transportation Systems: Architectures, Evidence, and Deployment Challenges Not What You’ve Signed Up For: Compromising Real-World LLM-Integrated Applications with Indirect Prompt Injection,

Reference 37

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This paper cites Road Vehicles—Functional Safety—Part 1: Vocabulary,.

Large Multimodal Agents for Intelligent Transportation Systems: Architectures, Evidence, and Deployment Challenges Road Vehicles—Functional Safety—Part 1: Vocabulary,

Reference 38

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Large Multimodal Agents for Intelligent Transportation Systems: Architectures, Evidence, and Deployment Challenges Road Vehicles—Safety of the Intended Functionality,

Reference 39

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Large Multimodal Agents for Intelligent Transportation Systems: Architectures, Evidence, and Deployment Challenges Road Vehicles—Cybersecurity Engineering,

Reference 40

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This paper cites Road Vehicles—Safety and Artificial Intelligence,.

Large Multimodal Agents for Intelligent Transportation Systems: Architectures, Evidence, and Deployment Challenges Road Vehicles—Safety and Artificial Intelligence,

Reference 41

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This paper cites IEEE Standard for Transparency of Autonomous Sys- tems,.

Large Multimodal Agents for Intelligent Transportation Systems: Architectures, Evidence, and Deployment Challenges IEEE Standard for Transparency of Autonomous Sys- tems,

Reference 42

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Observation bedee0eb-c902-43c1-9cc1-133aa454edf1 · outbound

This paper cites Artificial Intelligence Risk Management Framework (AI RMF 1.0),.

Large Multimodal Agents for Intelligent Transportation Systems: Architectures, Evidence, and Deployment Challenges Artificial Intelligence Risk Management Framework (AI RMF 1.0),

Reference 43

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This paper cites Information Technology—Artificial Intelligence— Guidance on Risk Management,.

Large Multimodal Agents for Intelligent Transportation Systems: Architectures, Evidence, and Deployment Challenges Information Technology—Artificial Intelligence— Guidance on Risk Management,

Reference 44

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This paper cites Information Technology—Artificial Intelligence— Management System,.

Large Multimodal Agents for Intelligent Transportation Systems: Architectures, Evidence, and Deployment Challenges Information Technology—Artificial Intelligence— Management System,

Reference 45

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Observation 5c8dbb9d-c48a-4701-9ab7-c875fa9e98cf · outbound

This paper cites Scalability in perception for autonomous driving: Waymo Open Dataset,.

Large Multimodal Agents for Intelligent Transportation Systems: Architectures, Evidence, and Deployment Challenges Scalability in perception for autonomous driving: Waymo Open Dataset,

Reference 47

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This paper cites The highD Dataset: A Drone Dataset of Naturalistic Vehicle Trajectories on German Highways for ValidationofHighlyAutomatedDrivingSystems,.

Large Multimodal Agents for Intelligent Transportation Systems: Architectures, Evidence, and Deployment Challenges The highD Dataset: A Drone Dataset of Naturalistic Vehicle Trajectories on German Highways for ValidationofHighlyAutomatedDrivingSystems,

Reference 48

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Observation 558082cb-c4e8-4d2c-aa09-f07f13b3c2e3 · outbound

This paper cites CityFlow: A Multi-Agent Reinforcement Learn- ing Environment for Large Scale City Traffic Scenario,.

Large Multimodal Agents for Intelligent Transportation Systems: Architectures, Evidence, and Deployment Challenges CityFlow: A Multi-Agent Reinforcement Learn- ing Environment for Large Scale City Traffic Scenario,

Reference 49

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This paper cites Microscopic Traffic Simulation Using SUMO,.

Large Multimodal Agents for Intelligent Transportation Systems: Architectures, Evidence, and Deployment Challenges Microscopic Traffic Simulation Using SUMO,

Reference 50

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This paper cites Large models for intelligent transportation systems and autonomous vehicles: A survey,.

Large Multimodal Agents for Intelligent Transportation Systems: Architectures, Evidence, and Deployment Challenges Large models for intelligent transportation systems and autonomous vehicles: A survey,

Reference 51

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This paper cites TrafficMind: A system- oriented review of large language models for intelligent trans- portation systems,.

Large Multimodal Agents for Intelligent Transportation Systems: Architectures, Evidence, and Deployment Challenges TrafficMind: A system- oriented review of large language models for intelligent trans- portation systems,

Reference 52

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Observation 8b5ad9d4-ae9c-4c70-8bcb-2729bb3c6f28 · outbound

This paper cites Foundation models for autonomous driving: A comprehensive survey,.

Large Multimodal Agents for Intelligent Transportation Systems: Architectures, Evidence, and Deployment Challenges Foundation models for autonomous driving: A comprehensive survey,

Reference 53

Resolution: metadata mismatch; raw_fallback, observed 2026-08-12T00:22:26.574455Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.

Observation 851aab75-290b-466e-aeef-a670a7609088 · outbound

This paper cites The role of large language models (LLMs) in enhancing intelligent transportation systems: A survey,.

Large Multimodal Agents for Intelligent Transportation Systems: Architectures, Evidence, and Deployment Challenges The role of large language models (LLMs) in enhancing intelligent transportation systems: A survey,

Reference 54

Resolution: unresolved; no resolver link, observed 2026-08-12T00:22:25.344906Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 440d0b76-a2dc-481d-bd7b-01b5167c41b5 · outbound

This paper cites Integrating LLMs with ITS: Recent advances, potentials, challenges, and future directions,.

Large Multimodal Agents for Intelligent Transportation Systems: Architectures, Evidence, and Deployment Challenges Integrating LLMs with ITS: Recent advances, potentials, challenges, and future directions,

Reference 55

Resolution: unresolved; no resolver link, observed 2026-08-12T00:22:25.349971Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation fa8939b7-a4ae-4845-9323-c07cd28ebb1c · outbound

This paper cites DiLu: A Knowledge-Driven Approach to Au- tonomous Driving with Large Language Models,.

Large Multimodal Agents for Intelligent Transportation Systems: Architectures, Evidence, and Deployment Challenges DiLu: A Knowledge-Driven Approach to Au- tonomous Driving with Large Language Models,

Reference 56

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T00:22:30.140748Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.

Observation a51b89f1-c89d-4a26-8ecb-6ad677cdddca · outbound

This paper cites Dolphins: Multimodal Language Model for Driving,.

Large Multimodal Agents for Intelligent Transportation Systems: Architectures, Evidence, and Deployment Challenges Dolphins: Multimodal Language Model for Driving,

Reference 57

Resolution: verified exact; doi, observed 2026-08-12T00:22:25.587753Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.

Observation fb1106c1-94bb-40ca-9924-ba86561b68bf · outbound

This paper cites Driving Everywhere with Large Language Model PolicyAdaptation,.

Large Multimodal Agents for Intelligent Transportation Systems: Architectures, Evidence, and Deployment Challenges Driving Everywhere with Large Language Model PolicyAdaptation,

Reference 58

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T00:22:30.124092Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.

Observation 8521df57-2aa7-4e3e-bace-e3851a426be3 · outbound

This paper cites LaMPilot: An Open Benchmark Dataset for Autonomous Driving with Language Model Programs,.

Large Multimodal Agents for Intelligent Transportation Systems: Architectures, Evidence, and Deployment Challenges LaMPilot: An Open Benchmark Dataset for Autonomous Driving with Language Model Programs,

Reference 59

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T00:22:30.108536Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.

Observation 161878f1-264a-41e4-8b16-0cc8f7b296d5 · outbound

This paper cites ChatScene: Knowledge-Enabled Safety-Critical Scenario Generation for Autonomous Vehicles,.

Large Multimodal Agents for Intelligent Transportation Systems: Architectures, Evidence, and Deployment Challenges ChatScene: Knowledge-Enabled Safety-Critical Scenario Generation for Autonomous Vehicles,

Reference 60

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T00:22:30.093481Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.

Observation ec662a91-6e86-48a9-b85e-04c6b5b5501e · outbound

This paper cites Editable Scene Simulation for Autonomous Driving via Collaborative LLM-Agents,.

Large Multimodal Agents for Intelligent Transportation Systems: Architectures, Evidence, and Deployment Challenges Editable Scene Simulation for Autonomous Driving via Collaborative LLM-Agents,

Reference 61

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T00:22:30.078715Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.

Observation ff45adc6-a01e-4fe1-a8df-3eb7243f35ee · outbound

This paper cites LLMScenario: Large Language Model Driven Scenario Generation,.

Large Multimodal Agents for Intelligent Transportation Systems: Architectures, Evidence, and Deployment Challenges LLMScenario: Large Language Model Driven Scenario Generation,

Reference 62

Resolution: unresolved; no resolver link, observed 2026-08-12T00:22:25.385167Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 89a303e1-bb1e-43d1-9225-55290c41d2db · outbound

This paper cites DriveVLM: The Convergence of Autonomous Driving and Large Vision-Language Models,.

Large Multimodal Agents for Intelligent Transportation Systems: Architectures, Evidence, and Deployment Challenges DriveVLM: The Convergence of Autonomous Driving and Large Vision-Language Models,

Reference 63

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T00:22:30.063024Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.

Observation 21f1d0c8-20b4-4eba-8fd9-f17eaebb2cad · outbound

This paper cites DriveGPT4-V2: Harnessing Large Language Model Capabilities for Enhanced Closed-Loop Autonomous Driving,.

Large Multimodal Agents for Intelligent Transportation Systems: Architectures, Evidence, and Deployment Challenges DriveGPT4-V2: Harnessing Large Language Model Capabilities for Enhanced Closed-Loop Autonomous Driving,

Reference 64

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T00:22:30.045540Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.

Observation 5416927b-f4c0-4e77-a924-b06d55bdca90 · outbound

This paper cites ORION: A Holistic End-to-End Autonomous Driving Framework by Vision-Language Instructed Action Gen- eration,.

Large Multimodal Agents for Intelligent Transportation Systems: Architectures, Evidence, and Deployment Challenges ORION: A Holistic End-to-End Autonomous Driving Framework by Vision-Language Instructed Action Gen- eration,

Reference 65

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T00:22:30.029198Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.

Observation 4d84beec-8739-4704-b7e7-782f80b8b601 · outbound

This paper cites GATSim: Urban Mobility Simula- tion with Generative Agents,.

Large Multimodal Agents for Intelligent Transportation Systems: Architectures, Evidence, and Deployment Challenges GATSim: Urban Mobility Simula- tion with Generative Agents,

Reference 66

Resolution: metadata mismatch; raw_fallback, observed 2026-08-12T00:22:26.256158Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.

Observation abf93dbe-c639-4ada-8a36-6fc0a255c56f · outbound

This paper cites AITP: Traffic Accident Responsibility Allocation via Multimodal Large Language Models,.

Large Multimodal Agents for Intelligent Transportation Systems: Architectures, Evidence, and Deployment Challenges AITP: Traffic Accident Responsibility Allocation via Multimodal Large Language Models,

Reference 67

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T00:22:30.011002Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.

Observation 80d35eaf-a4ee-4d40-973f-c6b4986a5958 · outbound

This paper cites MindDriver: Introducing Progressive Multi- modal Reasoning for Autonomous Driving,.

Large Multimodal Agents for Intelligent Transportation Systems: Architectures, Evidence, and Deployment Challenges MindDriver: Introducing Progressive Multi- modal Reasoning for Autonomous Driving,

Reference 68

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T00:22:29.993821Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.

Observation 3c8c5838-5393-4f41-9707-8ff3c5b18a64 · outbound

This paper cites V2X-UniPool: Unifying Multimodal Perception and Knowledge Reasoning for Autonomous Driving,.

Large Multimodal Agents for Intelligent Transportation Systems: Architectures, Evidence, and Deployment Challenges V2X-UniPool: Unifying Multimodal Perception and Knowledge Reasoning for Autonomous Driving,

Reference 69

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T00:22:29.976307Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.

Observation cf722f53-0581-49c4-9b9f-e1a9742e234a · outbound

This paper cites Large Language Model as Parking Planning Agent in the Context of Mixed Period of Autonomous Vehicles and Human-Driven Vehicles,.

Large Multimodal Agents for Intelligent Transportation Systems: Architectures, Evidence, and Deployment Challenges Large Language Model as Parking Planning Agent in the Context of Mixed Period of Autonomous Vehicles and Human-Driven Vehicles,

Reference 70

Resolution: unresolved; no resolver link, observed 2026-08-12T00:22:25.426502Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 890fe525-7b1e-48bd-a1fa-54ca45f1fd7c · outbound

This paper cites Bridging AI and Traffic Simulation: A Robust and Comprehensive Framework for LLM-Based AI Replanning Agents in MATSim,.

Large Multimodal Agents for Intelligent Transportation Systems: Architectures, Evidence, and Deployment Challenges Bridging AI and Traffic Simulation: A Robust and Comprehensive Framework for LLM-Based AI Replanning Agents in MATSim,

Reference 71

Resolution: verified exact; doi, observed 2026-08-12T00:22:25.568465Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.

Observation 7df4079c-b961-4bca-8175-020fb288ffb1 · outbound

This paper cites Agentic Traffic Intelligence: Augmented Human-in-the- Loop Scenario Generation for Microscopic Traffic Simulation,.

Large Multimodal Agents for Intelligent Transportation Systems: Architectures, Evidence, and Deployment Challenges Agentic Traffic Intelligence: Augmented Human-in-the- Loop Scenario Generation for Microscopic Traffic Simulation,

Reference 72

Resolution: metadata mismatch; raw_fallback, observed 2026-08-12T00:22:26.081900Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.

Observation 49f9d591-bca2-4c5a-94c4-22939e670bc3 · outbound

This paper cites Large Language Model-Assisted Multi-Objective Optimization for an Integrated Multimodal E-Mobility Platform,.

Large Multimodal Agents for Intelligent Transportation Systems: Architectures, Evidence, and Deployment Challenges Large Language Model-Assisted Multi-Objective Optimization for an Integrated Multimodal E-Mobility Platform,

Reference 73

Resolution: unresolved; no resolver link, observed 2026-08-12T00:22:25.442183Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation d40006f2-c19e-400f-bf5e-4b09384df5ea · outbound

This paper cites Use of cumulants to quantify uncertainties in the HBT measurements of the homogeneity regions.

Large Multimodal Agents for Intelligent Transportation Systems: Architectures, Evidence, and Deployment Challenges Use of cumulants to quantify uncertainties in the HBT measurements of the homogeneity regions

Reference 74

Resolution: unresolved; no resolver link, observed 2026-08-12T00:22:25.446932Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation df5de257-d8ec-43f0-96cc-211979944133 · outbound

This paper cites An Efficient Simulation Scene Generation Method BasedonExtractedRoadNetworkTopologyandLargeLanguage Models,.

Large Multimodal Agents for Intelligent Transportation Systems: Architectures, Evidence, and Deployment Challenges An Efficient Simulation Scene Generation Method BasedonExtractedRoadNetworkTopologyandLargeLanguage Models,

Reference 75

Resolution: verified exact; doi, observed 2026-08-12T00:22:25.551195Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.

Observation 55c5cfc1-0ad3-4455-a729-65b0b3119c04 · outbound

This paper cites DriveGPT4:InterpretableEnd-to-EndAutonomous Driving Via Large Language Model,.

Large Multimodal Agents for Intelligent Transportation Systems: Architectures, Evidence, and Deployment Challenges DriveGPT4:InterpretableEnd-to-EndAutonomous Driving Via Large Language Model,

Reference 76

Resolution: unresolved; no resolver link, observed 2026-08-12T00:22:25.457420Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 1a9447cd-8dfd-4435-ba53-16e63f110680 · outbound

This paper cites ChatSUMO Agent: An LLM- Based Agent for Conversational Traffic Simulation in SUMO,.

Large Multimodal Agents for Intelligent Transportation Systems: Architectures, Evidence, and Deployment Challenges ChatSUMO Agent: An LLM- Based Agent for Conversational Traffic Simulation in SUMO,

Reference 77

Resolution: verified exact; doi, observed 2026-08-12T00:22:25.532384Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.

Observation 21b3c436-1a60-4d13-a713-0980ca7b3f14 · outbound

This paper cites Generalizing End-to-End Autonomous Driving in Real-World Environments Using Zero-Shot LLMs,.

Large Multimodal Agents for Intelligent Transportation Systems: Architectures, Evidence, and Deployment Challenges Generalizing End-to-End Autonomous Driving in Real-World Environments Using Zero-Shot LLMs,

Reference 78

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T00:22:29.958937Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.

Observation 80afedb2-3159-4f4b-a2b9-7e4aff9a9671 · outbound

This paper cites Automating the Loop in Traffic Incident Manage- ment on Highway,.

Large Multimodal Agents for Intelligent Transportation Systems: Architectures, Evidence, and Deployment Challenges Automating the Loop in Traffic Incident Manage- ment on Highway,

Reference 79

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T00:22:29.942235Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.

Observation 2c9aacd0-5954-4317-9945-5670011e20d6 · outbound

This paper cites Promptable Closed-Loop Traffic Simulation,.

Large Multimodal Agents for Intelligent Transportation Systems: Architectures, Evidence, and Deployment Challenges Promptable Closed-Loop Traffic Simulation,

Reference 80

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T00:22:29.925916Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.

Observation 5b3e8c1f-d550-46f6-892e-5c111575de2a · outbound

This paper cites DriveCombo: Benchmarking Compositional Traffic Rule Reasoning in Autonomous Driving,.

Large Multimodal Agents for Intelligent Transportation Systems: Architectures, Evidence, and Deployment Challenges DriveCombo: Benchmarking Compositional Traffic Rule Reasoning in Autonomous Driving,

Reference 81

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T00:22:29.909656Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.

Observation 5f7ffc32-0e9e-476a-a520-e870d7ed4c59 · outbound

This paper cites Available: https://openaccess.thecvf.com/conten t/CVPR2026/html/Ma_DriveCombo_Benchmarking_Compo sitional_Traffic_Rule_Reasoning_in_Autonomous_Driving_ CVPR_2026_paper.html.

Large Multimodal Agents for Intelligent Transportation Systems: Architectures, Evidence, and Deployment Challenges Available: https://openaccess.thecvf.com/conten t/CVPR2026/html/Ma_DriveCombo_Benchmarking_Compo sitional_Traffic_Rule_Reasoning_in_Autonomous_Driving_ CVPR_2026_paper.html

Reference 2026

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T00:22:29.893063Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.

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Reference resolution

Outbound references

Pith citing papers