“Advanced Traffic Scene Risk Assessment Using Large Language Models with Causal Inference and Chain-of-Thought Reasoning” – ScienceDirect.com

The study presents a novel large language model (LLM) system designed to enhance traffic scene risk assessment by integrating causal inference and Chain-of-Thought reasoning. The approach leverages LLMs to analyze and understand complex traffic scenarios, enabling it to identify potential risks and outcomes effectively. By employing causal inference, the system can discern relationships and underlying factors influencing traffic incidents. The Chain-of-Thought reasoning allows for step-by-step analysis and logical connections between events, improving decision-making. The framework aims to bolster traffic safety and inform preventive measures by providing insights into risk factors. Overall, this innovative system combines advanced AI techniques to enhance the understanding and management of traffic risks, promising significant advancements in road safety protocols and applications.

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