Vehicle Trajectory Prediction Method Based on Intersection Context and Deep Belief Network

Tun LI; Yaokun ZHU; Xinhong WU; Yunpeng XIAO; Haifeng WU

doi:10.11999/JEIT200137

Volume 43 Issue 5

May 2021

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Article Navigation > Journal of Electronics & Information Technology > 2021 > 43(5): 1323-1330

Tun LI, Yaokun ZHU, Xinhong WU, Yunpeng XIAO, Haifeng WU. Vehicle Trajectory Prediction Method Based on Intersection Context and Deep Belief Network[J]. Journal of Electronics & Information Technology, 2021, 43(5): 1323-1330. doi: 10.11999/JEIT200137

Citation:

Tun LI, Yaokun ZHU, Xinhong WU, Yunpeng XIAO, Haifeng WU. Vehicle Trajectory Prediction Method Based on Intersection Context and Deep Belief Network[J]. Journal of Electronics & Information Technology, 2021, 43(5): 1323-1330. doi: 10.11999/JEIT200137

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Vehicle Trajectory Prediction Method Based on Intersection Context and Deep Belief Network

doi: 10.11999/JEIT200137

1.
School of Computer Science and Technology, Chongqing University of Posts and Telecommunications, Chongqing 400065, China
2.
Haikou Meteorological Service, Haikou 571199, China

Funds: The National Natural Science Foundation of China (61772098), The Science and Technology Research Program of Chongqing Municipal Education Commission (KJQN201800641), The Doctoral Top Talents Program of Chongqing University of Posts and Telecommunications (BYJS2017004), Chongqing Technology Innovation and Application Development Special General Project (cstc2020jscx-msxmX0150)

Received Date: 2020-02-28
Rev Recd Date: 2020-10-15

Available Online: 2020-10-21

Publish Date: 2021-05-18

Abstract

Abstract

For the temporal features of trajectory intersection sequence and spatial correlation of the actual road network, a trajectory prediction method based on the Deep Belief Networks and SoftMax (DBN-SoftMax) is proposed. At first, considering the sparsity of trajectory in an intersection set and the insufficiency of generalization ability in general feature learning methods for new features, the strong unsupervised feature learning ability of Deep Belief Network (DBN) is used to extract the local spatial features of trajectory. Secondly, considering the temporal features of the trajectory, the logistic regression method and the linear combination of the current trajectory set in the road network features space are used to predict the trajectory. Finally, Based on the idea of word embedding in the field of natural language processing and the contextual relationship of intersections in the actual trajectory, the vector set of intersections is used to represent the spatiotemporal relationship of traffic between intersections. The experimental results show that the model can not only extract the trajectory features effectively, but also obtain better prediction performance in a road network with complex topology.
- Intelligent traffic,
- Trajectory prediction,
- Trajectory intersection context analysis,
- Feature extract,
- Deep Belief Network(DBN)

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References(20)

References

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