The application provides a world model-based unmanned logistics vehicle intelligent driving model training method and
system. The method generates instructions by obtaining historical video data and long-
tail scene data, merges the historical video data with a preset amount of
Internet video data as sample data, trains a preset architecture generative world model through the sample data, determines a structured generation instruction, controls the first generative world model to generate multi-
modal long-
tail scene data as an input condition, determines first long-
tail scene state data, and trains and evaluates the first generative world model through the first long-tail scene state data to obtain a teacher model, determines a first student model, and deploys the first student model on a vehicle end to make
decision control during unmanned logistics
vehicle driving. The application effectively solves the problems of difficult long-tail
data acquisition and high cost, and improves the accuracy of long-tail scene recognition of the student model.