Multimodal Large Language Models for Enhanced Traffic Safety: A Comprehensive Review and Future Trends | Paper Citation Record

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This paper cites Road traffic Injuries.

Multimodal Large Language Models for Enhanced Traffic Safety: A Comprehensive Review and Future Trends Road traffic Injuries

Reference 1

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This paper cites Leveraging Deep Learning and Multimodal Large Language Models for Near -Miss Detection Using Crowdsourced Videos,.

Multimodal Large Language Models for Enhanced Traffic Safety: A Comprehensive Review and Future Trends Leveraging Deep Learning and Multimodal Large Language Models for Near -Miss Detection Using Crowdsourced Videos,

Reference 2

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This paper cites Advancing Object Detection in Transportation with Multimodal Large Language Models (MLLMs): A Comprehensive Review and Empirical Testing.

Multimodal Large Language Models for Enhanced Traffic Safety: A Comprehensive Review and Future Trends Advancing Object Detection in Transportation with Multimodal Large Language Models (MLLMs): A Comprehensive Review and Empirical Testing

Reference 3

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This paper cites Adversarial examples: attacks and defenses in the physical world,.

Multimodal Large Language Models for Enhanced Traffic Safety: A Comprehensive Review and Future Trends Adversarial examples: attacks and defenses in the physical world,

Reference 4

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

Multimodal Large Language Models for Enhanced Traffic Safety: A Comprehensive Review and Future Trends Using Multimodal Large Language Models (MLLMs) for Automated Detection of Traffic Safety-Critical Events,

Reference 5

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This paper cites A Comprehensive Survey of Multimodal Large Language Models: Concept, Application and Safety,.

Multimodal Large Language Models for Enhanced Traffic Safety: A Comprehensive Review and Future Trends A Comprehensive Survey of Multimodal Large Language Models: Concept, Application and Safety,

Reference 7

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This paper cites Adversarial Sensor Attack on LiDAR-based Perception in Autonomous Driving,.

Multimodal Large Language Models for Enhanced Traffic Safety: A Comprehensive Review and Future Trends Adversarial Sensor Attack on LiDAR-based Perception in Autonomous Driving,

Reference 8

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This paper cites MM -LLMs: Recent Advances in MultiModal Large Language Models,.

Multimodal Large Language Models for Enhanced Traffic Safety: A Comprehensive Review and Future Trends MM -LLMs: Recent Advances in MultiModal Large Language Models,

Reference 9

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This paper cites Integrating LLMs With ITS: Recent Advances, Potentials, Challenges, and Future Directions,.

Multimodal Large Language Models for Enhanced Traffic Safety: A Comprehensive Review and Future Trends Integrating LLMs With ITS: Recent Advances, Potentials, Challenges, and Future Directions,

Reference 10

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This paper cites A Review of Current Trends, Techniques, and Challenges in Large Language Models (LLMs),.

Multimodal Large Language Models for Enhanced Traffic Safety: A Comprehensive Review and Future Trends A Review of Current Trends, Techniques, and Challenges in Large Language Models (LLMs),

Reference 11

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This paper cites Vision meets robotics: The KITTI dataset,.

Multimodal Large Language Models for Enhanced Traffic Safety: A Comprehensive Review and Future Trends Vision meets robotics: The KITTI dataset,

Reference 12

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This paper cites CAFuser: Condition -Aware Multimodal Fusion for Robust Semantic Perception of Driving Scenes,.

Multimodal Large Language Models for Enhanced Traffic Safety: A Comprehensive Review and Future Trends CAFuser: Condition -Aware Multimodal Fusion for Robust Semantic Perception of Driving Scenes,

Reference 13

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This paper cites A survey of GPT -3 family large language models including ChatGPT and GPT-4,.

Multimodal Large Language Models for Enhanced Traffic Safety: A Comprehensive Review and Future Trends A survey of GPT -3 family large language models including ChatGPT and GPT-4,

Reference 15

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This paper cites MobileVLM V2: Faster and Stronger Baseline for Vision Language Model,.

Multimodal Large Language Models for Enhanced Traffic Safety: A Comprehensive Review and Future Trends MobileVLM V2: Faster and Stronger Baseline for Vision Language Model,

Reference 16

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This paper cites MobileNets: Efficient Convolutional Neural Networks for Mobile Vision Applications,.

Multimodal Large Language Models for Enhanced Traffic Safety: A Comprehensive Review and Future Trends MobileNets: Efficient Convolutional Neural Networks for Mobile Vision Applications,

Reference 17

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This paper cites Dynamic -LLaVA: Efficient Multimodal Large Language Models via Dynamic Vision -language Context Sparsification,.

Multimodal Large Language Models for Enhanced Traffic Safety: A Comprehensive Review and Future Trends Dynamic -LLaVA: Efficient Multimodal Large Language Models via Dynamic Vision -language Context Sparsification,

Reference 18

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Observation 6838aa71-110f-482f-b389-f98b6f49f2c2 · outbound

This paper cites Physical Adversarial Attacks on an Aerial Imagery Object Detector,.

Multimodal Large Language Models for Enhanced Traffic Safety: A Comprehensive Review and Future Trends Physical Adversarial Attacks on an Aerial Imagery Object Detector,

Reference 19

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This paper cites On the Adversarial Robustness of Multi -Modal Foundation Models,.

Multimodal Large Language Models for Enhanced Traffic Safety: A Comprehensive Review and Future Trends On the Adversarial Robustness of Multi -Modal Foundation Models,

Reference 20

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This paper cites Visual Reasoning and Multi-Agent Approach in Multimodal Large Language Models (MLLMs): Solving TSP and mTSP Combinatorial Challenges,.

Multimodal Large Language Models for Enhanced Traffic Safety: A Comprehensive Review and Future Trends Visual Reasoning and Multi-Agent Approach in Multimodal Large Language Models (MLLMs): Solving TSP and mTSP Combinatorial Challenges,

Reference 21

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This paper cites Benchmarking LLMs for Real -World Applications: From Numerical Metrics to Contextual and Qualitative Evaluation,.

Multimodal Large Language Models for Enhanced Traffic Safety: A Comprehensive Review and Future Trends Benchmarking LLMs for Real -World Applications: From Numerical Metrics to Contextual and Qualitative Evaluation,

Reference 22

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This paper cites Forging Vision Foundation Models for Autonomous Driving: Challenges, Methodologies, and Opportunities,.

Multimodal Large Language Models for Enhanced Traffic Safety: A Comprehensive Review and Future Trends Forging Vision Foundation Models for Autonomous Driving: Challenges, Methodologies, and Opportunities,

Reference 23

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This paper cites LLaVA -ST: A Multimodal Large Language Model for Fine -Grained Spatial-Temporal Understanding,.

Multimodal Large Language Models for Enhanced Traffic Safety: A Comprehensive Review and Future Trends LLaVA -ST: A Multimodal Large Language Model for Fine -Grained Spatial-Temporal Understanding,

Reference 24

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This paper cites ST-Align: A Multimodal Foundation Model for Image -Gene Alignment in Spatial Transcriptomics,.

Multimodal Large Language Models for Enhanced Traffic Safety: A Comprehensive Review and Future Trends ST-Align: A Multimodal Foundation Model for Image -Gene Alignment in Spatial Transcriptomics,

Reference 25

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This paper cites When language and vision meet road safety: leveraging multimodal large language models for video -based traffic accident analysis,.

Multimodal Large Language Models for Enhanced Traffic Safety: A Comprehensive Review and Future Trends When language and vision meet road safety: leveraging multimodal large language models for video -based traffic accident analysis,

Reference 26

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Observation cf43a1f0-4f28-4617-bcae-cb2bfa1c440c · outbound

This paper cites DRAMA: Joint Risk Localization and Captioning in Driving,.

Multimodal Large Language Models for Enhanced Traffic Safety: A Comprehensive Review and Future Trends DRAMA: Joint Risk Localization and Captioning in Driving,

Reference 27

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Observation 13938de6-90c7-4e8c-8a5a-9e3d3c67e416 · outbound

This paper cites Driving with LLMs: Fusing Object -Level Vector Modality for Explainable Autonomous Driving,.

Multimodal Large Language Models for Enhanced Traffic Safety: A Comprehensive Review and Future Trends Driving with LLMs: Fusing Object -Level Vector Modality for Explainable Autonomous Driving,

Reference 28

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This paper cites TrafficGPT : Viewing, processing and interacting with traffic foundation models,.

Multimodal Large Language Models for Enhanced Traffic Safety: A Comprehensive Review and Future Trends TrafficGPT : Viewing, processing and interacting with traffic foundation models,

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Multimodal Large Language Models for Enhanced Traffic Safety: A Comprehensive Review and Future Trends AccidentGPT: A V2X Environmental Perception Multi -modal Large Model for Accident Analysis and Prevention,

Reference 30

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Multimodal Large Language Models for Enhanced Traffic Safety: A Comprehensive Review and Future Trends Drive As You Speak: Enabling Human-Like Interaction With Large Language Models in Autonomous Vehicles,

Reference 31

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This paper cites Probing the Robustness of Vision-Language Pretrained Models: A Multimodal Adversarial Attack Approach,.

Multimodal Large Language Models for Enhanced Traffic Safety: A Comprehensive Review and Future Trends Probing the Robustness of Vision-Language Pretrained Models: A Multimodal Adversarial Attack Approach,

Reference 32

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This paper cites Gemini: A Family of Highly Capable Multimodal Models,.

Multimodal Large Language Models for Enhanced Traffic Safety: A Comprehensive Review and Future Trends Gemini: A Family of Highly Capable Multimodal Models,

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Multimodal Large Language Models for Enhanced Traffic Safety: A Comprehensive Review and Future Trends Visual Instruction Tuning,

Reference 34

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This paper cites Hallucination of Multimodal Large Language Models: A Survey,.

Multimodal Large Language Models for Enhanced Traffic Safety: A Comprehensive Review and Future Trends Hallucination of Multimodal Large Language Models: A Survey,

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This paper cites A Cloud -Edge Collaborative Architecture for Multimodal LLMs-Based Advanced Driver Assistance Systems in IoT Networks,.

Multimodal Large Language Models for Enhanced Traffic Safety: A Comprehensive Review and Future Trends A Cloud -Edge Collaborative Architecture for Multimodal LLMs-Based Advanced Driver Assistance Systems in IoT Networks,

Reference 36

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This paper cites SurrealDriver: Designing LLM -powered Generative Driver Agent Framework based on Human Drivers’ Driving -thinking Data,.

Multimodal Large Language Models for Enhanced Traffic Safety: A Comprehensive Review and Future Trends SurrealDriver: Designing LLM -powered Generative Driver Agent Framework based on Human Drivers’ Driving -thinking Data,

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This paper cites The Cityscapes Dataset for Semantic Urban Scene Understanding,.

Multimodal Large Language Models for Enhanced Traffic Safety: A Comprehensive Review and Future Trends The Cityscapes Dataset for Semantic Urban Scene Understanding,

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This paper cites Scalability in Perception for Autonomous Driving: Waymo Open Dataset,.

Multimodal Large Language Models for Enhanced Traffic Safety: A Comprehensive Review and Future Trends Scalability in Perception for Autonomous Driving: Waymo Open Dataset,

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This paper cites Graph neural networks for road safety modeling: datasets and evaluations for accident analysis,.

Multimodal Large Language Models for Enhanced Traffic Safety: A Comprehensive Review and Future Trends Graph neural networks for road safety modeling: datasets and evaluations for accident analysis,

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This paper cites Traffic Condition Classification Model Based on Traffic‐Net,.

Multimodal Large Language Models for Enhanced Traffic Safety: A Comprehensive Review and Future Trends Traffic Condition Classification Model Based on Traffic‐Net,

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This paper cites TrafficMOT: A Challenging Dataset for Multi -Object Tracking in Complex Traffic Scenarios,.

Multimodal Large Language Models for Enhanced Traffic Safety: A Comprehensive Review and Future Trends TrafficMOT: A Challenging Dataset for Multi -Object Tracking in Complex Traffic Scenarios,

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This paper cites Leveraging Multimodal Large Language Models (MLLMs) for Enhanced Object Detection and Scene Understanding in Thermal Images for Autonomous Driving Systems,.

Multimodal Large Language Models for Enhanced Traffic Safety: A Comprehensive Review and Future Trends Leveraging Multimodal Large Language Models (MLLMs) for Enhanced Object Detection and Scene Understanding in Thermal Images for Autonomous Driving Systems,

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This paper cites TAD: A Large -Scale Benchmark for Traffic Accidents Detection From Video Surveillance,.

Multimodal Large Language Models for Enhanced Traffic Safety: A Comprehensive Review and Future Trends TAD: A Large -Scale Benchmark for Traffic Accidents Detection From Video Surveillance,

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This paper cites Description of the SHRP 2 naturalistic database and the crash, near -crash, and baseline data sets,.

Multimodal Large Language Models for Enhanced Traffic Safety: A Comprehensive Review and Future Trends Description of the SHRP 2 naturalistic database and the crash, near -crash, and baseline data sets,

Reference 45

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This paper cites ScVLM: Enhancing Vision-Language Model for Safety-Critical Event Understanding,.

Multimodal Large Language Models for Enhanced Traffic Safety: A Comprehensive Review and Future Trends ScVLM: Enhancing Vision-Language Model for Safety-Critical Event Understanding,

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This paper cites Vision-Language Models for Autonomous Driving: CLIP-Based Dynamic Scene Understanding.

Multimodal Large Language Models for Enhanced Traffic Safety: A Comprehensive Review and Future Trends Vision-Language Models for Autonomous Driving: CLIP-Based Dynamic Scene Understanding

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This paper cites A Causality -Aware Paradigm for Evaluating Creativity of Multimodal Large Language Models,.

Multimodal Large Language Models for Enhanced Traffic Safety: A Comprehensive Review and Future Trends A Causality -Aware Paradigm for Evaluating Creativity of Multimodal Large Language Models,

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

Multimodal Large Language Models for Enhanced Traffic Safety: A Comprehensive Review and Future Trends Large Language Models (LLMs) as Traffic Control Systems at Urban Intersections: A New Paradigm,

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This paper cites Leveraging Large Language Models (LLMs) for Traffic Management at Urban Intersections: The Case of Mixed Traffic Scenarios.

Multimodal Large Language Models for Enhanced Traffic Safety: A Comprehensive Review and Future Trends Leveraging Large Language Models (LLMs) for Traffic Management at Urban Intersections: The Case of Mixed Traffic Scenarios

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This paper cites On Fairness of Unified Multimodal Large Language Model for Image Generation,.

Multimodal Large Language Models for Enhanced Traffic Safety: A Comprehensive Review and Future Trends On Fairness of Unified Multimodal Large Language Model for Image Generation,

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Resolution: verified fuzzy; raw_fallback, observed 2026-08-16T11:31:02.704688Z

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This paper cites MM-SafetyBench: A Benchmark for Safety Evaluation of Multimodal Large Language Models,.

Multimodal Large Language Models for Enhanced Traffic Safety: A Comprehensive Review and Future Trends MM-SafetyBench: A Benchmark for Safety Evaluation of Multimodal Large Language Models,

Reference 52

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Unavailable: canonical work link unavailable.

Observation b7073da2-1d0a-4e13-b760-5f7ce0294a2f · outbound

This paper cites Shielded Representations: Protecting Sensitive Attributes Through Iterative Gradient -Based Projection,.

Multimodal Large Language Models for Enhanced Traffic Safety: A Comprehensive Review and Future Trends Shielded Representations: Protecting Sensitive Attributes Through Iterative Gradient -Based Projection,

Reference 53

Resolution: unresolved; no resolver link, observed 2026-08-16T11:31:01.682519Z

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Unavailable: canonical work link unavailable.

Observation fbe17f73-2303-4c50-b49d-924e1a020264 · outbound

This paper cites MLLM -Protector: Ensuring MLLM’s Safety without Hurting Performance,.

Multimodal Large Language Models for Enhanced Traffic Safety: A Comprehensive Review and Future Trends MLLM -Protector: Ensuring MLLM’s Safety without Hurting Performance,

Reference 54

Resolution: unresolved; no resolver link, observed 2026-08-16T11:31:01.686232Z

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Unavailable: canonical work link unavailable.

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This paper cites AdaShield : Safeguarding Multimodal Large Language Models from Structure-Based Attack via Adaptive Shield Prompting,.

Multimodal Large Language Models for Enhanced Traffic Safety: A Comprehensive Review and Future Trends AdaShield : Safeguarding Multimodal Large Language Models from Structure-Based Attack via Adaptive Shield Prompting,

Reference 55

Resolution: verified exact; doi, observed 2026-08-16T11:31:01.738297Z

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Observation e1fa1abe-021d-459b-a8f6-497827280503 · outbound

This paper cites Towards More Robust Retrieval- Augmented Generation: Evaluating RAG Under Adversarial Poisoning Attacks,.

Multimodal Large Language Models for Enhanced Traffic Safety: A Comprehensive Review and Future Trends Towards More Robust Retrieval- Augmented Generation: Evaluating RAG Under Adversarial Poisoning Attacks,

Reference 56

Resolution: verified fuzzy; raw_fallback, observed 2026-08-16T11:31:02.692389Z

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No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.

Observation d9dcae9d-8c09-4384-b6e8-9035dbd3577e · outbound

This paper cites ACEA Position Paper Artificial Intelligence in the automobile industry,.

Multimodal Large Language Models for Enhanced Traffic Safety: A Comprehensive Review and Future Trends ACEA Position Paper Artificial Intelligence in the automobile industry,

Reference 57

Resolution: verified fuzzy; raw_fallback, observed 2026-08-16T11:31:02.679426Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.

Observation a1d7e4d4-d6e8-4bac-91b8-41939d7aac84 · outbound

This paper cites Cross-Domain Few- Shot In -Context Learning For Enhancing Traffic Sign Recognition,.

Multimodal Large Language Models for Enhanced Traffic Safety: A Comprehensive Review and Future Trends Cross-Domain Few- Shot In -Context Learning For Enhancing Traffic Sign Recognition,

Reference 58

Resolution: verified fuzzy; raw_fallback, observed 2026-08-16T11:31:02.666774Z

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No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.

Observation 92d1c9b0-3c5f-44f7-ba18-ca96096477df · outbound

This paper cites an unresolved cited work.

Multimodal Large Language Models for Enhanced Traffic Safety: A Comprehensive Review and Future Trends Unresolved cited work

Reference 2570

Resolution: unresolved; no resolver link, observed 2026-08-16T11:31:01.705044Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

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