Vehicle track prediction method and device

Abstract

The invention is applicable to the technical field of intelligent transportation and provides a vehicle track prediction method. The method comprises the following steps: acquiring history track dataof multiple vehicles in a preset time period, performing preprocessing on the history track data, thus obtaining a time-space diagram sequence corresponding to the history track data, wherein the time-space diagram sequence comprises time-space diagrams respectively corresponding to moments which are sequentially arranged in the preset time period, and each time-space diagram comprises nodes corresponding to at least three vehicles; and inputting the time-space diagram sequence into a trained prediction model to be processed, thus obtaining a corresponding predicted driving track of each vehicle, wherein the prediction model is obtained by training a Long Short-term Memory (LSTM) network on the basis of sample time-space diagrams corresponding to sample track data of multiple sample vehicles in the same time period and a sample driving track corresponding to each sample vehicle. The invention also provides a vehicle track prediction device and terminal equipment, vehicle track prediction precision and flexibility of the prediction model are improved, and robustness is also enhanced, thereby being better applied to manless driving.

Description

technical field [0001] The present application belongs to the technical field of intelligent transportation, and in particular relates to a method and device for predicting the trajectory of a vehicle. Background technique [0002] Vehicle trajectories are the most comprehensive and complete expression of the state of traffic flow. They can not only reflect the vehicle's driving path on the road network, but also reflect the variation of vehicle speed with time and space. It contains very rich traffic flow information. [0003] At present, the vehicle trajectory prediction focuses on the extraction and fusion of the interaction information between the target vehicle and the surrounding vehicles (the interaction of data in time series and space and the semantic relationship). The traditional extraction method is only based on the historical trajectory of the vehicle, and does not capture the interaction between the vehicles, and cannot perceive the relative position. This mak...

AI Technical Summary

Problems solved by technology

[0004] The embodiment of the present application provides a vehicle trajectory prediction method and device, which can solve the problem that the existing research ignores the correlation between time and space and makes the accuracy of vehicle trajectory prediction not high

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