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7 September 2023 Behavior trajectory prediction research of adjacent vehicles based on LSTM
Jinjie Cheng, Ruobing Duan, Hang Zeng, Ruijian Cai, Ruilin Qiu, Zhiping Wan
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Proceedings Volume 12790, Eighth International Conference on Electromechanical Control Technology and Transportation (ICECTT 2023); 127904V (2023) https://doi.org/10.1117/12.2689845
Event: 8th International Conference on Electromechanical Control Technology and Transportation (ICECTT 2023), 2023, Hangzhou, China
Abstract
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.
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Jinjie Cheng, Ruobing Duan, Hang Zeng, Ruijian Cai, Ruilin Qiu, and Zhiping Wan "Behavior trajectory prediction research of adjacent vehicles based on LSTM", Proc. SPIE 12790, Eighth International Conference on Electromechanical Control Technology and Transportation (ICECTT 2023), 127904V (7 September 2023); https://doi.org/10.1117/12.2689845
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KEYWORDS
Autonomous vehicles
Convolution
Data hiding
Evolutionary algorithms
Safety
Unmanned vehicles
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