Vehicle trajectory prediction method based on environmental attention neural network model

A neural network model and vehicle trajectory technology, which is applied in the field of vehicle trajectory prediction based on the environmental attention neural network model, can solve the problems that the environmental interaction features are not completely sufficient, and the extraction of environmental features is single, so as to achieve good trajectory prediction effect and improve accuracy. Effect of sex, effect improvement
CN112215337APending Publication Date: 2021-01-12JIANGSU UNIV
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Patent Information

Authority / Receiving Office
CN · China
Current Assignee / Owner
JIANGSU UNIV
Publication Date
2021-01-12

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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.
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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...

Claims

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