Crash Risk Prediction Guided by Large Language Models Using Pre-Crash Trajectories
The proposed framework converts LLM reasoning into structured supervision and distills it into a lightweight temporal graph network, which is then calibrated with real crash data, and suggests that the proposed framework can improve prediction performance and data efficiency when real crash data are limited.