A trajectory prediction method, device, storage medium and electronic equipment for obstacles

Abstract

This specification discloses a trajectory prediction method, device, storage medium, and electronic equipment for obstacles. In the embodiment of this specification, the global interaction features are obtained by inputting the historical trajectory of each obstacle into the first model, and the two or more scales The map data and the historical trajectory of the obstacle to be predicted are input into the second model to obtain the map aggregation feature corresponding to the obstacle to be predicted, and then the global interaction feature, map aggregation feature and the historical trajectory of the obstacle to be predicted are input into the third model , so that the third model can adaptively select the velocity of the obstacle to be predicted from the map aggregation features aggregated with maps of various scales through the movement speed of the obstacle to be predicted expressed by the historical trajectory of the obstacle to be predicted. The map feature adapted to the moving speed is used to assist in predicting the moving track of the obstacle to be predicted, so that the predicted moving track of the obstacle to be predicted is more accurate.

Description

Technical field[0001]This specification relates to the field of unmanned driving technology, and in particular to an obstacle trajectory prediction method, device, storage medium and electronic equipment.Background technique[0002]At present, in the field of unmanned driving technology, obstacle trajectory prediction is an important technology. With the development of sensor technology in recent years, more accurate maps (such as radar maps, high-precision maps, etc.) are used to assist in predicting the trajectory of obstacles.[0003]In the prior art, when a map is used to assist the trajectory prediction of an obstacle, for obstacles of different speeds, maps of the same scale are used to assist the trajectory prediction of the obstacle.[0004]But in fact, for obstacles with different speeds, the required map scales are also different. For example, if the speed is faster, you generally need to see farther, while at low speed, you only need to observe the closer scene.[0005]Therefore,...

AI Technical Summary

Problems solved by technology

[0005] Therefore, the method in the prior art that uses maps of the same scale to assist obstacle trajectory prediction will lead to inaccurate prediction results

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