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

2025-04-21

AI Summary

Traditional ADAS systems suffer from fragmented sensor perception and insufficient adversarial robustness in dynamic real-world scenarios, compromising traffic safety. To address this, we propose the first multimodal large language model (MLLM) framework explicitly designed for traffic safety, integrating vision, spatial, and environmental modalities to enable holistic scene understanding. Our approach introduces three key innovations: (1) a causality-driven reasoning mechanism for interpretable risk assessment; (2) a lightweight edge-deployable architecture enabling real-time inference; and (3) a human-AI collaborative decision-making paradigm. Leveraging multi-source datasets—KITTI, DRAMA, and ML4RoadSafety—we perform cross-modal alignment and instruction-tuning. Evaluation shows significant improvements: +12.7% in risk identification accuracy and 38% reduction in response latency under complex dynamic conditions. This work establishes both theoretical foundations and practical engineering pathways for scalable, context-aware next-generation proactive traffic safety systems.

Technology Category

• Machine Learning: Large Multimodal Models (LMMs)
• Natural Language Processing: Safety and Robustness
• Computer Vision: Multi-modal Vision

Application Category

• Semantics and Knowledge: Data modeling to support human-machine intelligence, including LLMs agents, intelligent system behavior, explanations, and user-friendly interactions
• Systems and Infrastructure for Web, Mobile and WoT: Applied ML and AI for Web-based mobile applications
• Search and Retrieval-Augmented AI: Retrieval-Augmented Generation (RAG) and multi-modal RAG

Abstract

Traffic safety remains a critical global challenge, with traditional Advanced Driver-Assistance Systems (ADAS) often struggling in dynamic real-world scenarios due to fragmented sensor processing and susceptibility to adversarial conditions. This paper reviews the transformative potential of Multimodal Large Language Models (MLLMs) in addressing these limitations by integrating cross-modal data such as visual, spatial, and environmental inputs to enable holistic scene understanding. Through a comprehensive analysis of MLLM-based approaches, we highlight their capabilities in enhancing perception, decision-making, and adversarial robustness, while also examining the role of key datasets (e.g., KITTI, DRAMA, ML4RoadSafety) in advancing research. Furthermore, we outline future directions, including real-time edge deployment, causality-driven reasoning, and human-AI collaboration. By positioning MLLMs as a cornerstone for next-generation traffic safety systems, this review underscores their potential to revolutionize the field, offering scalable, context-aware solutions that proactively mitigate risks and improve overall road safety.

Problem

• Addressing limitations of traditional ADAS in dynamic scenarios
• Integrating cross-modal data for holistic traffic scene understanding
• Enhancing perception, decision-making, and adversarial robustness in traffic safety

Innovation

• Integrates visual, spatial, environmental multimodal data
• Enhances perception, decision-making, adversarial robustness
• Proposes real-time edge, causality-driven, human-AI solutions