Pedestrian trajectory prediction method based on long-term and short-term memory

A long-short-term memory and trajectory prediction technology, which is applied in prediction, neural learning methods, biological neural network models, etc., can solve problems such as prediction errors

Active Publication Date: 2020-06-19
GUANGDONG UNIV OF TECH
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AI Technical Summary

Problems solved by technology

Therefore, there is a huge forecast error
Therefore, the above trajectory prediction methods still have certain limitations, and there is a possibility of breakthrough

Method used

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  • Pedestrian trajectory prediction method based on long-term and short-term memory
  • Pedestrian trajectory prediction method based on long-term and short-term memory
  • Pedestrian trajectory prediction method based on long-term and short-term memory

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Embodiment Construction

[0053] Below in conjunction with accompanying drawing, the present invention is described in detail.

[0054] The present invention will be further described below in conjunction with the accompanying drawings.

[0055] Such as figure 2 As shown in , the position information of the pedestrians on the zebra crossing is collected by the marking software in the intersection environment.

[0056] Such as image 3 As shown, the collected location information is exported to Microsoft Excel to obtain the initial data.

[0057] Such as Figure 4As shown, the data is processed into a matrix of [number of pedestrians, 4] through operations such as screening, deduplication, time conversion, pedestrian number conversion, and time interval sampling. The first column represents the frame id at the time of collection, the second column represents the pedestrian number pedid, the third column represents the abscissa x of the pedestrian, and the fourth column represents the ordinate y of ...

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Abstract

The invention discloses a pedestrian trajectory prediction method based on long-term and short-term memory, which mainly comprises the following steps: preprocessing data, and converting the data intoa matrix [pedestrian number, 4]; an attention mechanism is introduced to select information influencing indexes such as the walking direction and speed of the current pedestrian, and all current position information is connected through a full connection layer; inputting the historical state hidden information of the global pedestrians in the same scene into a pooling layer for pooling to achievethe purpose of sharing the global hidden information; converting the pooling tensor of the historical state hidden information of all pedestrians in the current state, the position information of thecurrent pedestrian and the information which is selected by the attention mechanism and influences the pedestrians into long-term and short-term memory sequence information through a long-term and short-term memory unit; and converting the current state information into a coordinate space through a multi-layer perceptron structure to generate a prediction track sequence.

Description

technical field [0001] The invention relates to a method for predicting pedestrian trajectories, which is based on long-term and short-term memory and is suitable for predicting the future trajectories of pedestrians in complex scenes. Background technique [0002] With the rise of the artificial intelligence industry, unmanned driving has gradually entered people's lives. In recent years, many companies at home and abroad with top science and technology are vigorously developing the unmanned driving industry, such as BYD Group in China, Tesla Motors in the United States... and want to develop the field of unmanned driving, First of all, it is necessary to establish a good pedestrian trajectory prediction system. This is because on the road, pedestrians and vehicles are in the same scene, and when pedestrians encounter obstacles, they will use their own brains to judge that they need to slow down, avoid obstacles or is to stop. When a self-driving car encounters a conflict...

Claims

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Application Information

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IPC IPC(8): G06Q10/04G06N3/04G06N3/08
CPCG06Q10/04G06N3/08G06N3/044G06N3/045Y02T10/40
Inventor 陈漪皓曾伟良姚若愚黎曦琦郑宇凡朱明洲
Owner GUANGDONG UNIV OF TECH
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