Trajectory prediction of cyclist based on dynamic Bayesian network and long short-term memory model at unsignalized intersections
Cyclist trajectory prediction is of great significance for both active collision avoidance and path planning of intelligent vehicles. This paper presents a trajectory prediction method for the motion intention of cyclists in real traffic scenarios. This method is based on dynamic Bayesian network (D...
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| Veröffentlicht in: | Science China. Information sciences 2021-07, Vol.64 (7), p.172207, Article 172207 |
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| creator | Gao, Hongbo Su, Hang Cai, Yingfeng Wu, Renfei Hao, Zhengyuan Xu, Yongneng Wu, Wei Wang, Jianqing Li, Zhijun Kan, Zhen |
| description | Cyclist trajectory prediction is of great significance for both active collision avoidance and path planning of intelligent vehicles. This paper presents a trajectory prediction method for the motion intention of cyclists in real traffic scenarios. This method is based on dynamic Bayesian network (DBN) and long short-term memory (LSTM). The motion intention of cyclists is hard to predict owing to potential large uncertainties. The DBN is used to infer the distribution of cyclists’ intentions at intersections to improve the prediction time. The LSTM with encoder-decoder is used to predict the cyclists’ trajectories to improve the accuracy of prediction. Therefore, the DBN and LSTM are adopted to guarantee prediction accuracy and improve the prediction time. The experiment results are presented to show the effectiveness of the predict strategies. |
| doi_str_mv | 10.1007/s11432-020-3071-8 |
| format | Article |
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This paper presents a trajectory prediction method for the motion intention of cyclists in real traffic scenarios. This method is based on dynamic Bayesian network (DBN) and long short-term memory (LSTM). The motion intention of cyclists is hard to predict owing to potential large uncertainties. The DBN is used to infer the distribution of cyclists’ intentions at intersections to improve the prediction time. The LSTM with encoder-decoder is used to predict the cyclists’ trajectories to improve the accuracy of prediction. Therefore, the DBN and LSTM are adopted to guarantee prediction accuracy and improve the prediction time. The experiment results are presented to show the effectiveness of the predict strategies.</description><identifier>ISSN: 1674-733X</identifier><identifier>EISSN: 1869-1919</identifier><identifier>DOI: 10.1007/s11432-020-3071-8</identifier><language>eng</language><publisher>Beijing: Science China Press</publisher><subject>Accuracy ; Automation ; Bayesian analysis ; Collision avoidance ; Computer Science ; Dynamical systems ; Encoders-Decoders ; Information Systems and Communication Service ; Intelligent vehicles ; Kalman filters ; Research Paper ; Science ; Traffic ; Traffic intersections ; Trajectories ; Vehicles ; Velocity</subject><ispartof>Science China. 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Information sciences</title><addtitle>Sci. China Inf. Sci</addtitle><description>Cyclist trajectory prediction is of great significance for both active collision avoidance and path planning of intelligent vehicles. This paper presents a trajectory prediction method for the motion intention of cyclists in real traffic scenarios. This method is based on dynamic Bayesian network (DBN) and long short-term memory (LSTM). The motion intention of cyclists is hard to predict owing to potential large uncertainties. The DBN is used to infer the distribution of cyclists’ intentions at intersections to improve the prediction time. The LSTM with encoder-decoder is used to predict the cyclists’ trajectories to improve the accuracy of prediction. Therefore, the DBN and LSTM are adopted to guarantee prediction accuracy and improve the prediction time. The experiment results are presented to show the effectiveness of the predict strategies.</description><subject>Accuracy</subject><subject>Automation</subject><subject>Bayesian analysis</subject><subject>Collision avoidance</subject><subject>Computer Science</subject><subject>Dynamical systems</subject><subject>Encoders-Decoders</subject><subject>Information Systems and Communication Service</subject><subject>Intelligent vehicles</subject><subject>Kalman filters</subject><subject>Research Paper</subject><subject>Science</subject><subject>Traffic</subject><subject>Traffic intersections</subject><subject>Trajectories</subject><subject>Vehicles</subject><subject>Velocity</subject><issn>1674-733X</issn><issn>1869-1919</issn><fulltext>true</fulltext><rsrctype>article</rsrctype><creationdate>2021</creationdate><recordtype>article</recordtype><sourceid>AFKRA</sourceid><sourceid>AZQEC</sourceid><sourceid>BENPR</sourceid><sourceid>CCPQU</sourceid><sourceid>DWQXO</sourceid><sourceid>GNUQQ</sourceid><recordid>eNp1kE9LAzEQxRdRsNR-AG8Bz9HMZrfZHLX4DwpeKngL2SRbU3eTmqTIevObm1rBk3OZYXjvzfArinMgl0AIu4oAFS0xKQmmhAFujooJNHOOgQM_zvOcVZhR-nJazGLckFyUkpI1k-JrFeTGqOTDiLbBaKuS9Q75DqlR9TYm1MpoNMo7PTo5WIVu5GiilQ45kz58eEPSadR7t0bx1YeEkwkDGsywjxy8Nj2SCe1ctGsne_uZw6zLmmh-TsWz4qSTfTSz3z4tnu9uV4sHvHy6f1xcL7GqKEmYSc1USyi0lHCoy2reUl41mrEaGoCW05oqophqZMe7WrdKS8MrDhVwIus5nRYXh9xt8O87E5PY-F3IL0VR8kyrYrzZq-CgUsHHGEwntsEOMowCiNjDFgfYIsMWe9iiyZ7y4IlZ69Ym_CX_b_oGfJOD2g</recordid><startdate>20210701</startdate><enddate>20210701</enddate><creator>Gao, Hongbo</creator><creator>Su, Hang</creator><creator>Cai, Yingfeng</creator><creator>Wu, Renfei</creator><creator>Hao, Zhengyuan</creator><creator>Xu, Yongneng</creator><creator>Wu, Wei</creator><creator>Wang, Jianqing</creator><creator>Li, Zhijun</creator><creator>Kan, Zhen</creator><general>Science China Press</general><general>Springer Nature B.V</general><scope>AAYXX</scope><scope>CITATION</scope><scope>8FE</scope><scope>8FG</scope><scope>AFKRA</scope><scope>ARAPS</scope><scope>AZQEC</scope><scope>BENPR</scope><scope>BGLVJ</scope><scope>CCPQU</scope><scope>DWQXO</scope><scope>GNUQQ</scope><scope>HCIFZ</scope><scope>JQ2</scope><scope>K7-</scope><scope>P5Z</scope><scope>P62</scope><scope>PQEST</scope><scope>PQQKQ</scope><scope>PQUKI</scope></search><sort><creationdate>20210701</creationdate><title>Trajectory prediction of cyclist based on dynamic Bayesian network and long short-term memory model at unsignalized intersections</title><author>Gao, Hongbo ; Su, Hang ; Cai, Yingfeng ; Wu, Renfei ; Hao, Zhengyuan ; Xu, Yongneng ; Wu, Wei ; Wang, Jianqing ; Li, Zhijun ; Kan, Zhen</author></sort><facets><frbrtype>5</frbrtype><frbrgroupid>cdi_FETCH-LOGICAL-c430t-7ad7cb031b30915246b3948d7751811b9353c0c7c8af9f5dbcdae94914190a563</frbrgroupid><rsrctype>articles</rsrctype><prefilter>articles</prefilter><language>eng</language><creationdate>2021</creationdate><topic>Accuracy</topic><topic>Automation</topic><topic>Bayesian analysis</topic><topic>Collision avoidance</topic><topic>Computer Science</topic><topic>Dynamical systems</topic><topic>Encoders-Decoders</topic><topic>Information Systems and Communication Service</topic><topic>Intelligent vehicles</topic><topic>Kalman filters</topic><topic>Research Paper</topic><topic>Science</topic><topic>Traffic</topic><topic>Traffic intersections</topic><topic>Trajectories</topic><topic>Vehicles</topic><topic>Velocity</topic><toplevel>peer_reviewed</toplevel><toplevel>online_resources</toplevel><creatorcontrib>Gao, Hongbo</creatorcontrib><creatorcontrib>Su, Hang</creatorcontrib><creatorcontrib>Cai, Yingfeng</creatorcontrib><creatorcontrib>Wu, Renfei</creatorcontrib><creatorcontrib>Hao, Zhengyuan</creatorcontrib><creatorcontrib>Xu, Yongneng</creatorcontrib><creatorcontrib>Wu, Wei</creatorcontrib><creatorcontrib>Wang, Jianqing</creatorcontrib><creatorcontrib>Li, Zhijun</creatorcontrib><creatorcontrib>Kan, Zhen</creatorcontrib><collection>CrossRef</collection><collection>ProQuest SciTech Collection</collection><collection>ProQuest Technology Collection</collection><collection>ProQuest Central UK/Ireland</collection><collection>Advanced Technologies & Aerospace Collection</collection><collection>ProQuest Central Essentials</collection><collection>ProQuest Central</collection><collection>Technology Collection</collection><collection>ProQuest One Community College</collection><collection>ProQuest Central Korea</collection><collection>ProQuest Central Student</collection><collection>SciTech Premium Collection</collection><collection>ProQuest Computer Science Collection</collection><collection>Computer Science Database</collection><collection>Advanced Technologies & Aerospace Database</collection><collection>ProQuest Advanced Technologies & Aerospace Collection</collection><collection>ProQuest One Academic Eastern Edition (DO NOT USE)</collection><collection>ProQuest One Academic</collection><collection>ProQuest One Academic UKI Edition</collection><jtitle>Science China. 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This paper presents a trajectory prediction method for the motion intention of cyclists in real traffic scenarios. This method is based on dynamic Bayesian network (DBN) and long short-term memory (LSTM). The motion intention of cyclists is hard to predict owing to potential large uncertainties. The DBN is used to infer the distribution of cyclists’ intentions at intersections to improve the prediction time. The LSTM with encoder-decoder is used to predict the cyclists’ trajectories to improve the accuracy of prediction. Therefore, the DBN and LSTM are adopted to guarantee prediction accuracy and improve the prediction time. The experiment results are presented to show the effectiveness of the predict strategies.</abstract><cop>Beijing</cop><pub>Science China Press</pub><doi>10.1007/s11432-020-3071-8</doi></addata></record> |
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| subjects | Accuracy Automation Bayesian analysis Collision avoidance Computer Science Dynamical systems Encoders-Decoders Information Systems and Communication Service Intelligent vehicles Kalman filters Research Paper Science Traffic Traffic intersections Trajectories Vehicles Velocity |
| title | Trajectory prediction of cyclist based on dynamic Bayesian network and long short-term memory model at unsignalized intersections |
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