Roadside end pedestrian trajectory prediction algorithm based on adversarial generative network

A trajectory prediction, pedestrian technology, applied in biological neural network model, prediction, calculation and other directions, can solve the problem of complex and diverse pedestrians

Pending Publication Date: 2021-02-09
CHANGZHOU UNIV +1
View PDF0 Cites 5 Cited by
  • Summary
  • Abstract
  • Description
  • Claims
  • Application Information

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

Method used

the structure of the environmentally friendly knitted fabric provided by the present invention; figure 2 Flow chart of the yarn wrapping machine for environmentally friendly knitted fabrics and storage devices; image 3 Is the parameter map of the yarn covering machine
View more

Image

Smart Image Click on the blue labels to locate them in the text.
Viewing Examples
Smart Image
  • Roadside end pedestrian trajectory prediction algorithm based on adversarial generative network
  • Roadside end pedestrian trajectory prediction algorithm based on adversarial generative network
  • Roadside end pedestrian trajectory prediction algorithm based on adversarial generative network

Examples

Experimental program
Comparison scheme
Effect test

Embodiment Construction

[0067] The present invention is described in further detail now in conjunction with accompanying drawing. These drawings are all simplified schematic diagrams, which only illustrate the basic structure of the present invention in a schematic manner, so they only show the configurations related to the present invention.

[0068] like Figure 1-Figure 4 A roadside pedestrian trajectory prediction algorithm based on an adversarial generative network is shown, including the following steps:

[0069] S10: use the encoder to encode the input track;

[0070] S20: Calculate pedestrian social attention by using pedestrian head orientation;

[0071] S30: Applying a latent variable predictor to generate a predictable latent variable distribution;

[0072] S40: Generate a predicted future trajectory of pedestrians;

[0073] S50: Use the discriminator to optimize the pedestrian trajectory generated by the generator;

[0074] The step 1 specifically includes the following steps:

[00...

the structure of the environmentally friendly knitted fabric provided by the present invention; figure 2 Flow chart of the yarn wrapping machine for environmentally friendly knitted fabrics and storage devices; image 3 Is the parameter map of the yarn covering machine
Login to view more

PUM

No PUM Login to view more

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...

Claims

the structure of the environmentally friendly knitted fabric provided by the present invention; figure 2 Flow chart of the yarn wrapping machine for environmentally friendly knitted fabrics and storage devices; image 3 Is the parameter map of the yarn covering machine
Login to view more

Application Information

Patent Timeline
no application Login to view more
IPC IPC(8): G06K9/00G06K9/62G06Q10/04G06N3/04
CPCG06Q10/04G06V40/103G06N3/045G06N3/044G06F18/214
Inventor 杨彪何才臻徐黎明闫国成吕继东陈阳
Owner CHANGZHOU UNIV
Who we serve
  • R&D Engineer
  • R&D Manager
  • IP Professional
Why Eureka
  • Industry Leading Data Capabilities
  • Powerful AI technology
  • Patent DNA Extraction
Social media
Try Eureka
PatSnap group products

Browse by: Latest US Patents, China's latest patents, Technical Efficacy Thesaurus, Application Domain, Technology Topic.

© 2024 PatSnap. All rights reserved.Legal|Privacy policy|Modern Slavery Act Transparency Statement|Sitemap