DATA-DRIVEN-BASED SYSTEM FOR IMPLEMENTING AUTOMATIC ITERATION OF PREDICTION MODEL

摘要

A data-driven-based system for implementing the automatic iteration of a prediction model, comprising a processor and a cloud. The processor labels behavior data of each road participant on the basis of an observed movement trajectory of each road participant to obtain corresponding labeling information, thus achieving automatic labeling, and then sends filtered first behavior data and the corresponding labeling information to the cloud. When determining that preset automatic trigger conditions are met, the cloud extracts training samples to train an initial network model and obtain a target network model, so that model training is automatically triggered, and then evaluation is automatically triggered. Moreover, when evaluation results meet model update requirements, the cloud sends the target network model to the processor to achieve the automatic deployment of the target network model. The processor automatically updates the prediction model to the target network model after receiving the target network model. Hence, no manual participation is required for data labeling, model training, model evaluation or model updating, and the degree of automation is high.