Vehicle Trajectory Prediction by Knowledge-Driven LSTM Network in Urban Environments

doi:10.1155/2020/8894060

	Vehicle Trajectory Prediction by Knowledge-Driven LSTM Network in Urban Environments
	Wang, Shaobo 1,2,3,4; Zhao, Pan 1,2,3; Yu, Biao 1,2,3; Huang, Weixin 1,2,3,4; Liang, Huawei 1,2,3
刊名	JOURNAL OF ADVANCED TRANSPORTATION
	2020-11-07
卷号	2020
ISSN号	0197-6729
DOI	10.1155/2020/8894060
通讯作者	Liang, Huawei(hwliang@iim.ac.cn)
英文摘要	An accurate prediction of future trajectories of surrounding vehicles can ensure safe and reasonable interaction between intelligent vehicles and other types of vehicles. Vehicle trajectories are not only constrained by a priori knowledge about road structure, traffic signs, and traffic rules but also affected by posterior knowledge about different driving styles of drivers. The existing prediction models cannot fully combine the prior and posterior knowledge in the driving scene and perform well only in a specific traffic scenario. This paper presents a long short-term memory (LSTM) neural network driven by knowledge. First, a driving knowledge base is constructed to describe the prior knowledge about a driving scenario. Then, the prediction reference baseline (PRB) based on driving knowledge base is determined by using the rule-based online reasoning system. Finally, the future trajectory of the target vehicle is predicted by an LSTM neural network based on the prediction reference baseline, while the predicted trajectory considers both posterior and prior knowledge without increasing the computation complexity. The experimental results show that the proposed trajectory prediction model can adapt to different driving scenarios and predict trajectories with high accuracy due to the unique combination of the prior and posterior knowledge in the driving scene.
资助项目	Institute of Applied Technology, Hefei Institute of Physical Science ; Academy of Sciences of China ; National Key Research and Development Program of China[2016YFD0701401] ; National Key Research and Development Program of China[2017YFD0700303] ; National Key Research and Development Program of China[2018YFD0700602] ; Youth Innovation Promotion Association of the Chinese Academy of Sciences[2017488] ; Key Supported Project in the Thirteenth Five-Year Plan of Hefei Institutes of Physical Science, Chinese Academy of Sciences ; Equipment Pre-Research Program[301060603] ; Natural Science Foundation of Anhui Province[1508085MF133] ; Technological Innovation Project for New Energy and Intelligent Networked Automobile Industry of Anhui Province
WOS关键词	MODEL
WOS研究方向	Engineering ; Transportation
语种	英语
出版者	WILEY-HINDAWI
WOS记录号	WOS:000594204300001
资助机构	Institute of Applied Technology, Hefei Institute of Physical Science ; Academy of Sciences of China ; National Key Research and Development Program of China ; Youth Innovation Promotion Association of the Chinese Academy of Sciences ; Key Supported Project in the Thirteenth Five-Year Plan of Hefei Institutes of Physical Science, Chinese Academy of Sciences ; Equipment Pre-Research Program ; Natural Science Foundation of Anhui Province ; Technological Innovation Project for New Energy and Intelligent Networked Automobile Industry of Anhui Province
内容类型	期刊论文
源URL	[http://ir.hfcas.ac.cn:8080/handle/334002/105472]
专题	中国科学院合肥物质科学研究院
通讯作者	Liang, Huawei
作者单位	1.Chinese Acad Sci, Hefei Inst Phys Sci, Hefei 230031, Peoples R China 2.Chinese Acad Sci, Innovat Res Inst Robot & Intelligent Mfg, Hefei, Anhui, Peoples R China 3.Anhui Engn Lab Intelligent Driving Technol & Appl, Hefei, Anhui, Peoples R China 4.Univ Sci & Technol China, Hefei 230026, Peoples R China
推荐引用方式 GB/T 7714	Wang, Shaobo,Zhao, Pan,Yu, Biao,et al. Vehicle Trajectory Prediction by Knowledge-Driven LSTM Network in Urban Environments[J]. JOURNAL OF ADVANCED TRANSPORTATION,2020,2020.
APA	Wang, Shaobo,Zhao, Pan,Yu, Biao,Huang, Weixin,&Liang, Huawei.(2020).Vehicle Trajectory Prediction by Knowledge-Driven LSTM Network in Urban Environments.JOURNAL OF ADVANCED TRANSPORTATION,2020.
MLA	Wang, Shaobo,et al."Vehicle Trajectory Prediction by Knowledge-Driven LSTM Network in Urban Environments".JOURNAL OF ADVANCED TRANSPORTATION 2020(2020).