Track predicting method and device, storage medium, driving system and vehicle

Abstract

The invention provides a track predicting method and device, a storage medium, a driving system and a vehicle. According to the method, through acquiring global semantic data and global track data ofan area with a to-be-predicted object (S202), then combining the global semantic data and the global track data, global combined data are obtained (S204), therefore, features in the global combined data are extracted for obtaining global features (S206). Furthermore, the global features are processed by means of a trained track predicting model, thereby obtaining a target track of the to-be-predicted object (S208). The method can realize track prediction of a moving object according to the global data and has relatively high prediction accuracy, and furthermore reduces generation probability of accidents to a certain extent.

Description

Technical field [0001] The present invention relates to the field of intelligent transportation technology, in particular to a trajectory prediction method and device, a storage medium, a driving system and a vehicle. Background technique [0002] With the development of the field of intelligent transportation, the prediction algorithm for the trajectory of moving objects is of great significance in the field of path planning. By predicting the motion trajectory of the moving object, path planning can be performed when the possible future motion trajectory of the moving object is known, which is beneficial to prevent accidents such as collisions. [0003] Current trajectory prediction algorithms are generally based on the motion data of the moving object itself, determine the applicable motion model of the moving object according to the category of the moving object, and use the motion model to process the motion data of the moving object itself, and then post-processing Integrati...

AI Technical Summary

Problems solved by technology

[0004] The existing trajectory prediction algorithm is based on the motion data of the moving object itself, and cannot predict the trajectory from a global perspective, which will easily lead to the intersection of the predicted trajectories of different moving objects, which will lead to collisions in path planning or scheduling based on this, etc. Accidents, there is a greater potential safety hazard

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