Vision-language and generative models in traffic video safety analysis: a computational framework and research agenda

Area of Science:

• Artificial Intelligence
• Computer Vision
• Machine Learning

Background:

• Vision-language and generative models are increasingly used for analyzing complex traffic video data.
• Existing approaches offer potential for enhancing road safety analysis but require integration and further development.

Purpose of the Study:

• To review and integrate progress in foundation vision-language models, multimodal large language models, and diffusion-based world models for traffic safety.
• To propose a unified taxonomy for applying these AI paradigms across different traffic safety analysis levels and environments.
• To identify key challenges and outline a research agenda for improving AI in traffic safety.

Main Methods:

• Review and synthesis of current research in vision-language, multimodal large language, and generative AI models for video analysis.
• Development of a taxonomy categorizing AI model applications in traffic safety (network monitoring, event analysis, scenario generation).
• Identification of technical challenges including hallucination, temporal consistency, and sim-to-real transfer.

Main Results:

• Integration of diverse AI paradigms (VLM, MLLM, temporal reasoning, diffusion models) for enhanced semantic understanding and causal reasoning in traffic safety.
• A proposed taxonomy mapping AI model families to network-scale monitoring, event-level analysis, and generative/counterfactual scenario assessment.
• Identification of critical challenges such as hallucination control, temporal consistency, sim-to-real transfer, and safety alignment.

Conclusions:

• These AI models offer significant potential for advancing traffic safety through richer interpretation and causal reasoning.
• Addressing identified challenges is crucial for developing grounded, interpretable, and efficient AI systems for real-world traffic safety.
• A structured research agenda is proposed to guide future development in multimodal AI for traffic safety applications.