A High-Density Composite Scene Trajectory Prediction Method Based on Heterogeneous Graph Aggregation Network

Abstract

The invention discloses a high-density compound scene trajectory prediction method based on a heterogeneous graph aggregation network, and constructs a heterogeneous graph aggregation network including three substructures of an adaptive neighbor selector, an encoder and a decoder. The selector automatically selects the neighbors of the target object and generates the structure of the heterogeneous graph; then the encoder uses a two-stage aggregator to aggregate heterogeneous feature information between different types of neighbors, and the decoder uses LSTM-based historical information The residual connection technology is used for decoding, and the output of the future two-dimensional coordinate time series prediction information of the target object is obtained by using the input historical trajectory time series information features. The invention can significantly improve the accuracy rate of track prediction in high-density composite scenes, and realizes high-precision track prediction of multi-category objects in complex traffic scenes.

Description

technical field [0001] The invention relates to the technical field of trajectory prediction, in particular to a high-density compound scene trajectory prediction method based on a heterogeneous graph aggregation network. Background technique [0002] With the rapid development of autonomous driving technology, a considerable part of motor vehicles will be replaced by autonomous vehicles in the future. In the field of autonomous driving, trajectory prediction is widely used as a core technology in the navigation, control and decision-making of autonomous vehicles. Self-driving vehicles control their own behavior more precisely by predicting the future trajectories of vehicles around them, thereby avoiding safety hazards such as traffic accidents. [0003] Therefore, autonomous vehicles need to use efficient and accurate trajectory prediction technology to make behavior decisions. [0004] Existing trajectory prediction methods are mainly divided into single-scene predictio...

AI Technical Summary

Problems solved by technology

[0008] In view of the above technical problems, the present invention provides a high-density composite scene trajectory prediction method based on a heterogeneous graph aggregation network to solve the weight sharing problem caused by a single neural network and the problem of multiple selection or missing selection of neighbors in a fixed area

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