Trajectory prediction of cyclist based on dynamic Bayesian network and long short-term memory model at unsignalized intersections

作     者：Hongbo GAO Hang SU Yingfeng CAI Renfei WU Zhengyuan HAO Yongneng XU Wei WU Jianqing WANG Zhijun LI Zhen KAN Hongbo GAO;Hang SU;Yingfeng CAI;Renfei WU;Zhengyuan HAO;Yongneng XU;Wei WU;Jianqing WANG;Zhijun LI;Zhen KAN

作者机构：Department of Automation University of Science and Technology of China Institute of Advanced Technology University of Science and Technology of China The Dipartimento di Elettronica Informazione e Bioingegneria Politecnico di Milano Automotive Engineering Research Institute Jiangsu University College of Traffic and Transportation Southeast University School of Automation Nanjing University of Science and Technology School of Vehicle and Mobility Tsinghua University 

出 版 物：《Science China(Information Sciences)》 (中国科学:信息科学(英文版))

年 卷 期：2021年第64卷第7期

页      面：104-116页

核心收录：

学科分类：0810[工学-信息与通信工程] 0808[工学-电气工程] 08[工学] 081104[工学-模式识别与智能系统] 082302[工学-交通信息工程及控制] 0811[工学-控制科学与工程] 0823[工学-交通运输工程] 0812[工学-计算机科学与技术（可授工学、理学学位）] 

基　　金：supported in part by National Natural Science Foundation of China (Grant Nos. U1804161, U2013601, U20A20225) Key Research and Development Plan of Anhui Province (Grant No. 202004a05020058) Fundamental Research Funds for the Central Universities, Science and Technology Innovation Planning Project of Ministry of Education of China, NVIDIA NVAIL program Key Laboratory of Advanced Perception and Intelligent Control of High-end Equipment of Ministry of Education (Anhui Polytechnic University, Wuhu, China, 241000) (Grant No. GDSC202007) 

主　　题：trajectory prediction dynamic Bayesian network (DBN) long short-term memory (LSTM) unsignalized intersections motion intention 

摘      要：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.