Roadside end pedestrian trajectory prediction algorithm based on adversarial generative network

Abstract

The invention relates to a roadside pedestrian trajectory generation algorithm based on an adversarial generative network. A multi-mode prediction trajectory is generated by using a social attention mechanism and a pedestrian trajectory latent variable; by adversarial generation training of a trajectory generator and a discriminator, capabilities of a generator and the discriminator are continuously optimized, and the trajectory generation accuracy of the generator is improved; a social attention mechanism based on the head orientation is provided, the head orientation of the pedestrian is obtained through the final speed direction of the pedestrian, the cosine value of the included angle between pedestrians is calculated according to the head orientation information, and the soft attention and hard attention mechanism optimizes the output of the social attention mechanism through the calculated angle information. Converging and outputting operation are carried out by a maximum poolinglayer. a new latent variable generation method is provided, two feedforward neural networks are used for learning latent variables from a pedestrian historical track and an observation track respectively, inputs of a latent variable generator comprise the position, the speed and the acceleration, and distribution of three kinds of latent variables is generated from the three inputs respectively.

Description

technical field [0001] The present invention relates to the technical field of automatic driving, and in particular to pedestrian trajectory prediction. The present invention provides a roadside pedestrian trajectory prediction algorithm based on an adversarial generation network. Background technique [0002] With the continuous development of robot automatic navigation and automobile automatic driving technology, unmanned driving technology has received extensive attention and has bright application prospects; unmanned vehicles can bring convenience to people's lives, but unmanned vehicles are driving It is necessary to monitor the trajectory of pedestrians on the road and predict the future trajectory of pedestrians so as to avoid collisions with pedestrians; in order to better predict the trajectory of pedestrians, unmanned vehicles need to process the observed pedestrian trajectory data and learn from it. According to the law of pedestrian movement, and predict the next...

AI Technical Summary

Problems solved by technology

[0005] The technical problem to be solved by the present invention is: in order to overcome the complex and diverse movement patterns of pedestrians in the prior art, and it is difficult to describe the complex pedestrian movement with a dynamic model, a roadside model based on confrontation generation network is provided. End-to-End Pedestrian Trajectory Prediction Algorithm

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