With the continuous development of the Internet of Vehicles, safety monitoring and assisted driving during vehicle operation has become the core of vehicle intelligence. In vehicle-assisted driving, state perception and trajectory prediction of surrounding vehicles is critical. Mathematical models mainly realize the traditional behavior and trajectory prediction of adjacent vehicles, and it is difficult to make reasonable use of the motion time series information, and it is difficult to predict the development trend further. In particular, traditional methods usually have some limitations, which limit the degree of real-time response and accuracy. This paper proposes an LSTM-based approach for predicting the behavior trajectory of adjacent vehicles. The network can autonomously learn the environmental relationship by constructing an adjacent vehicle interaction pool and digitizing the surrounding information. At the same time, the attention mechanism is comprehensively used to improve the accuracy and reliability of the prediction. It is expected that the research in this paper can help the research and optimization of vehicle intelligent assisted driving.
KEYWORDS: Neural networks, Education and training, Associative arrays, Process modeling, Deep learning, Neurons, Semantics, Mathematical optimization, Feature extraction, Data hiding
Since deep learning has been continuously developing, research on language models utilizing deep learning has increased, and certain results have been obtained. There are many research results, but RNN has unique advantages regarding the construction of language models. In addition, due to its weight-sharing cyclic network form, the language model can have the information memory function described above, which facilitates the prediction of content in the future. Text generation is one of the most common applications in this area. However, RNN may have some limitations, including its inability to remember the above for a prolonged period of time, making the language model formed by RNN relatively weak in terms of intelligence and ability to connect the languages under study. Therefore, this paper proposes to build a language prediction model based on the RNN network model LSTM. This will allow the language model to capture the previous characteristics better, resulting in improved intelligent text prediction.
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