Generative adversarial network model and vehicle trajectory prediction method using same

Abstract

The invention discloses a generative adversarial network model and a vehicle trajectory prediction method using the generative adversarial network model, the generative adversarial network model is composed of two modules: a first module: a generator of a generative adversarial network; and a second module: a discriminator of the generative adversarial network. The generator is used for generatinga simulation track, the discriminator is used for discriminating the authenticity of the track, the first module and the second module are in game confrontation with each other, and finally the generator capable of generating the simulation track highly similar to the real track is obtained. The generative adversarial network model is trained, and a data acquisition method, an optimizer, a learning rate, a training round, an iteration round and a loss function are given; finally, a method for verifying the accuracy of the trajectory generated by the vehicle trajectory prediction system is provided. According to the prediction method for track prediction by using the generative adversarial network provided by the invention, the past network structure is innovated, and the prediction accuracy is effectively improved.

Description

technical field [0001] The invention belongs to the field of intelligent traffic automatic driving, and in particular relates to a generative confrontation network model and a vehicle trajectory prediction method using the generative confrontation network model. Background technique [0002] With the development of society and the increasing travel demand of social groups, the smart car industry has ushered in a golden age of rapid development. At the same time, with the rapid development of science and technology, especially computer technology, the emergence of deep learning provides new ideas and methods for the future of smart cars. [0003] The National Highway Traffic Safety Administration (NHTSA) of the United States divides the level of automation of intelligent vehicles into five levels from l1 to l5, and the important evaluation standard for distinguishing the level of automation of intelligent vehicles is the decision-making ability of intelligent vehicles. Befor...

AI Technical Summary

Problems solved by technology

[0005] However, the prediction method using LSTM still has the problem of low accuracy

At the same time, in the past, the model failed to consider the impact of the trajectory of the vehicles around the predicted vehicle on the own vehicle, and there were problems of less input features and low robustness.

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