Motion track prediction method and device, computer equipment and storage medium

A technology of motion trajectory and prediction method, which is applied in computer components, calculation, neural learning methods, etc., can solve the problems that the correlation cannot be fully considered, and the accuracy of trajectory prediction cannot be further improved, so as to achieve the effect of improving prediction performance

Pending Publication Date: 2021-11-05
SHENZHEN DEEPROUTE AI CO LTD +1
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Problems solved by technology

This kind of line-level correlation cannot be fully considered, and the correlation between tra

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  • Motion track prediction method and device, computer equipment and storage medium
  • Motion track prediction method and device, computer equipment and storage medium
  • Motion track prediction method and device, computer equipment and storage medium

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[0063] For purposes of the present application, technical solutions and advantages clearer, the accompanying drawings and the following embodiments, the present application is further described in detail. It should be understood that the specific embodiments described herein are only intended to illustrate the present application is not intended to limit the present application.

[0064] In one embodiment, if figure 1 Shown, there is provided a method of trajectory prediction, the present embodiment the method is applied to a terminal (terminal may be but not limited to the vehicle device, the track mounted device on the vehicle data acquisition device) is illustrated, it is understood that the the method may also be applied to a server, it can also be applied to systems comprising a terminal and a server, and is achieved by the interaction of the terminal and a server. In this embodiment, the method comprises the steps of:

[0065] Step 102, acquiring the current traffic particip...

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Abstract

The invention relates to a motion track prediction method and device, computer equipment and a storage medium. The method comprises the following steps: acquiring a current motion track of a traffic participant and a current lane map related to the current track; according to the number of the track points on the current motion track, the first position information of each track point, the speed information and the identifier of the corresponding traffic participant, constructing the characteristics of the current traffic participant; constructing features of the current lane map according to the number of lane points of each lane map in the current lane map, the second position information and direction information of each lane point and the identifier of the corresponding lane; and performing high-dimensional embedded feature learning according to the features of the current traffic participant and the features of the current lane map to obtain target feature data considering the correlation between track points, between track points and lane points, and between lane points, and performing track prediction according to the target feature data to obtain a predicted track within a specified duration. By adopting the method, the accuracy of trajectory prediction can be improved.

Description

technical field [0001] The present application relates to the technical field of automatic driving, and in particular to a motion trajectory prediction method, device, computer equipment and storage medium. Background technique [0002] With the development of computer technology, automatic driving technology has emerged, which is gradually applied in various fields, such as ore mining, construction site inspection, substation inspection, etc.; in the process of automatic driving, it is necessary to predict the driving of other traffic participants The trajectory is used to plan the current driving trajectory to ensure that the autonomous driving can reach the destination safely and accurately. At present, Transformer technology can be applied to trajectory prediction tasks; Transformer is a deep network model that does not use recurrent neural network (RNN) or convolutional network (CNN), and fully represents input and output data through self-attention (self-attention) mec...

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Application Information

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IPC IPC(8): G06K9/00G06K9/46G06N3/04G06N3/08
CPCG06N3/084G06N3/044G06N3/045
Inventor 许家妙刘鹏
Owner SHENZHEN DEEPROUTE AI CO LTD
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