Vehicle trajectory prediction method based on uncertainty estimation

Abstract

The invention relates to a vehicle trajectory prediction method based on uncertainty estimation. The method comprises the steps of collecting the pose information and local semantic map information of surrounding vehicles in real time, and obtaining the historical pose information of the vehicles, according to the collected vehicle position information, in combination with a high-precision map, a lane connection relationship and a traffic rule, determining all candidate lanes of a future trajectory end point, evaluating the uncertainty of the historical pose of the vehicle according to the pose of the vehicle and the local semantic map, converting the historical pose of the vehicle to the coordinate system of each lane, conducting feature coding in combination with information such as lane directions, and predicting the probability of a vehicle driving end point on each candidate lane, and predicting probability distribution of a future driving route of the target vehicle according to the feature coding. Compared with the prior art, the method solves the problems that in the prior art, input vehicle historical pose uncertainty is neglected, and trajectory multi-mode modeling is incomplete, an accurate and reliable information source can be provided for downstream decision planning of automatic driving, and risks are reduced.

Description

technical field [0001] The invention relates to the technical field of automatic driving, in particular to a vehicle track prediction method based on uncertainty estimation. Background technique [0002] Autonomous driving technology has the advantages of significantly improving traffic safety and reducing traffic congestion, which has attracted more and more attention. In real driving scenarios, self-driving vehicles usually need to drive in a shared area with other vehicles. In order to cope with this complex traffic environment, self-driving technology not only needs to obtain the current pose of the surrounding vehicles, but also needs to understand the surrounding vehicles. Reliable prediction of the future trajectory of the vehicle can provide a basis for safe and efficient decision-making planning of autonomous vehicles. [0003] Currently, vehicle trajectory prediction techniques are mainly divided into single-modal methods and multi-modal methods. Among them, the ...

AI Technical Summary

Problems solved by technology

[0006] The purpose of the present invention is to provide a vehicle trajectory prediction method based on uncertainty estimation in order to overcome the above-mentioned defects in the prior art. Ignoring the problem of input vehicle historical pose uncertainty and trajectory multimodal modeling incompleteness

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