Vehicle trajectory prediction method based on environmental attention neural network model

Abstract

The invention discloses a vehicle trajectory prediction method based on an environmental attention neural network model, and constructs a model for increasing attention to each element in the environment, namely an environmental attention network (E-ANet) model. According to the model provided by the invention, transverse expansion is carried out on the basis of a structure in which an LSTM encoder and a convolutional social pool are connected in series, and a graph attention neural network and the convolutional social pool containing an SE module are added to form a parallel structure. Through the novel parallel structure, feature information updated by connecting edges of all nodes in a graph structure formed by the vehicle and the surrounding environment in the running process and feature information in a spatial position structure in the surrounding environment are captured. Compared with a convolution social pool model, the new model structure provided by the invention has the advantages that the effect of extracting the environment interaction information is greatly improved, and meanwhile, a better track prediction effect is achieved compared with other existing models.

Description

technical field [0001] The invention belongs to the field of vehicle intelligent driving, in particular to a vehicle trajectory prediction method based on an environmental attention neural network model. Background technique [0002] In recent years, smart cars, as an emerging field of continuous development, are providing more convenient and effective services for the society. With the advancement of smart car technology, smart systems such as vehicle collision avoidance systems and driver assistance systems have provided good help to drivers. Advanced intelligent systems enable drivers and passengers to drive vehicles in a safer and more comfortable traffic environment. [0003] The various systems contained in the smart car require the support of a large amount of surrounding environment information during operation. Because smart cars cannot fully reach the driving level of human drivers, and vehicles will always be in a traffic scene that is highly interactive with su...

AI Technical Summary

Problems solved by technology

The model used for vehicle trajectory prediction in the current research only considers the environmental interaction features within a certain structure, but the extraction of environmental features always only considers a single interaction structure, so that the extracted environmental interaction features are not completely sufficient

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