Vehicle position prediction method based on deep learning

Abstract

The present invention provides a vehicle position prediction method based on deep learning. The method comprises the steps of: S1, obtaining an original VLPR data set, performing cleaning and denoising of the original VLPR data set, obtaining a VLPR data set, and performing grouping of the VLPR data set according to a set time quantum; S2, from the VLPR data set, extracting vehicle pass record ofthe same vehicle, generating a vehicle track data set and screening out a track meeting a demand, and performing data conversion of the related feature information of the track; S3, establishing an algorithm model based on deep learning according to the obtained vehicle track features, and achieving analysis and learning of the track features; and S4, after performing feature learning of the vehicle track, employing a full-connection network layer to combine a Softmax classifier to output a next position vector, matching real geographic position information, outputting the real geographic position information, and achieving vehicle position prediction. The method provided by the invention analyzes operation features of a road network and employs the track features in the vehicle driving process so as to obtain high prediction precision.

Description

technical field [0001] The present invention relates to the field of intelligent transportation systems, in particular a method for predicting vehicle positions based on deep learning. Background technique [0002] On the one hand, the rapid development of the city has led to a geometrically rapid increase in the number of motor vehicles. On the other hand, it has brought huge challenges to urban management such as traffic congestion relief and illegal vehicle detection and control. Therefore, accurate prediction of vehicle location is of great significance to urban traffic safety and is a global concern. If the dynamic position of the vehicle can be accurately predicted, the traffic control department can reasonably arrange the police force to check the illegal vehicles, and remind the public to selectively avoid the congested areas, thereby reducing potential safety hazards. [0003] Given the importance of the vehicle location prediction problem, a lot of research work h...

AI Technical Summary

Problems solved by technology

[0004] Defects of existing methods or inventions: First, existing methods are mainly based on single-user mode, which is difficult to solve the cold start problem, and it is difficult to solve the prediction of rich multi-user trajectories

Second, existing methods rely on continuous data such as GPS information, and it is difficult to obtain vehicle trajectories for discrete, missing, and incomplete data.

Third, the existing methods have not effectively considered the important influence of situational factors such as weather and morning and evening peak patterns on location prediction

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