Motion track prediction method and device

Abstract

The embodiment of the invention discloses a motion track prediction method and device. The method comprises the steps of obtaining historical tracks and motion attribute information of traffic participation objects corresponding to a target object and corresponding current map information; determining a trajectory prediction feature corresponding to each traffic participation object by using a feature extraction layer of the target trajectory prediction model and the initial feature corresponding to each traffic participation object; using a feature extraction layer of the target trajectory prediction model and the trajectory prediction features corresponding to the traffic participants to determine hidden random variable multi-modal probability distribution corresponding to the traffic participants; and determining a multi-modal prediction trajectory corresponding to each traffic participation object by using a feature regression layer of the target trajectory prediction model, the trajectory prediction features corresponding to each traffic participation object and the hidden random variable multi-modal probability distribution corresponding to each traffic participation object so as to reduce the limitation of motion trajectory prediction. Therefore, the method can better adapt to a relatively complex automatic driving scene.

Description

technical field [0001] The present invention relates to the technical field of trajectory prediction, and in particular, to a method and device for predicting a motion trajectory. Background technique [0002] In the field of autonomous driving, when an autonomous vehicle is driving, it needs to refer to the future motion trajectories of the surrounding traffic participants to plan the driving trajectory of the self-driving vehicle to ensure the safety of the self-driving vehicle and the traffic participants. Correspondingly, it is very important for autonomous vehicles to accurately and timely predict the future motion trajectories of traffic participants. [0003] Considering that the future behavior of traffic participants has obvious uncertainty, that is, the future motion trajectories of traffic participants have obvious uncertainties. In related technologies, the probability of future motion trajectories of traffic participants is generally modeled by a mixed Gaussian ...

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Problems solved by technology

[0004] In the above process, it is necessary to manually design the number of categories and classification rules of the future motion trajectory predicted by the neural network model, which to a certain extent makes the predicted future motion trajectory limited and difficult to adapt to more complex autonomous driving scenarios.

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