Cro-intentformer-based method and system for predicting surrounding vehicle trajectories by integrating driving intentions

Abstract

The provided is a method and system for trajectory prediction based on Cro-IntentFormer. The method starts by preprocessing vehicle trajectory data collected by sensors to produce raw data suitable for model input. Vehicles are treated as nodes, and the distance between vehicles serves as the basis for determining whether there is an edge between two vehicle nodes. A physical relationship graph is constructed and, along with the raw data, input into a spatio-temporal feature extraction module to obtain the spatio-temporal features of the trajectory. The spatio-temporal feature matrix is then input into an intent prediction module to determine the predicted intentions of the vehicles. Based on the intent information output by the intent prediction module, a semantic relationship graph is reconstructed and input, along with the raw data, into the spatio-temporal feature extraction module to derive the semantic features of the trajectory.