Vehicle Trajectory Prediction Method based on Deep Learning under the background of Internet of Vehicles

Abstract

Listen

Translate article

With the expansion of the Internet, big data and other industries, mobile devices are almost used in all aspects of life. The location-based service platform provides convenient services for users, but also obtains a large number of user history tracks. Through the trajectory data mining, we can extract valuable user behavior information, help user’s better plan routes, and can be applied in urban traffic management and commercial advertising layout and other scenarios. In this paper, a trajectory prediction algorithm based on deep learning is proposed to solve the problem of vehicle trajectory prediction. In this paper, the vehicle passing records are preprocessed and the vehicle trajectory is generated. The trajectory is transformed into the discrete position sequence of the vehicle as the input of the algorithm. Then the space-time multi-dimensional features of vehicle trajectory are extracted. Finally, a trajectory prediction algorithm based on deep learning is constructed. The batch size is 256 and the average track length of training set is 46.45. The convolution neural network and deep bidirectional long-term memory network are fused to learn the local and global information of vehicle trajectory. The research shows that compared with traditional machine learning, deep learning has achieved better results in various fields of computer vision because of its powerful representation ability. The visual perception algorithm based on deep learning can accurately analyze the regularity of vehicle movement and make accurate prediction of vehicle moving trajectory in the future.