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

文献类型：期刊论文

作者Wang, Shaobo1,2,3,4; Zhao, Pan2,3,4; Yu, Biao2,3,4; Huang, Weixin1,2,3,4; Liang, Huawei2,3,4
刊名JOURNAL OF ADVANCED TRANSPORTATION
出版日期2020-11-07
卷号2020
ISSN号0197-6729
DOI10.1155/2020/8894060
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.
WOS关键词MODEL
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研究方向Engineering ; Transportation
语种英语
WOS记录号WOS:000594204300001
出版者WILEY-HINDAWI
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
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.Univ Sci & Technol China, Hefei 230026, Peoples R China
2.Anhui Engn Lab Intelligent Driving Technol & Appl, Hefei, Anhui, Peoples R China
3.Chinese Acad Sci, Innovat Res Inst Robot & Intelligent Mfg, Hefei, Anhui, Peoples R China
4.Chinese Acad Sci, Hefei Inst Phys Sci, Hefei 230031, 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).

入库方式： OAI收割

来源：合肥物质科学研究院

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