Motion trajectory prediction based on a CNN-LSTM sequential model

Accurate monitoring the surrounding environment is an important research direction in the field of unmanned systems such as bio-robotics, and has attracted much research attention in recent years. The trajectories of surrounding vehicles should be predicted accurately in space and time to realize ac...

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Bibliographic Details
Published in:Science China. Information sciences 2020-11, Vol.63 (11), p.212207, Article 212207
Main Authors: Xie, Guo, Shangguan, Anqi, Fei, Rong, Ji, Wenjiang, Ma, Weigang, Hei, Xinhong
Format: Article
Language:English
Subjects:
Artificial neural networks
Computer Science
Error correction
Information Systems and Communication Service
Neural networks
Research Paper
Robotics
Search algorithms
System effectiveness
Trajectories
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Summary:Accurate monitoring the surrounding environment is an important research direction in the field of unmanned systems such as bio-robotics, and has attracted much research attention in recent years. The trajectories of surrounding vehicles should be predicted accurately in space and time to realize active defense and running safety of an unmanned system. However, there is uncertainty and uncontrollability in the process of trajectory prediction of surrounding obstacles. In this study, we propose a trajectory prediction method based on a sequential model, that fuses two neural networks of a convolutional neural network (CNN) and a long short-term memory network (LSTM). First, a box plot is used to detect and eliminate abnormal values of vehicle trajectories, and valid trajectory data are obtained. Second, the trajectories of surrounding vehicles are predicted by merging the characteristics of CNN space expansion and LSTM time expansion; the hyper-parameters of the model are optimized according to a grid search algorithm, which satisfies the double-precision prediction requirement in space and time. Finally, data from next generation simulation (NGSIM) and Creteil roundabout in France are taken as test cases; the correctness and rationality of the method are verified by prediction error indicators. Experimental results demonstrate that the proposed CNN-LSTM method is more accurate and features a shorter time cost, which meets the prediction requirements and provides an effective method for the safe operation of unmanned systems.
ISSN:1674-733X
1869-1919
DOI:10.1007/s11432-019-2761-y