Motion trajectory prediction based on a CNN-LSTM sequential model
Motion trajectory prediction based on a CNN-LSTM sequential model作者机构：School of Automation and Information Engineering School of Computer Science and EngineeringXi'an University of Technology
出 版 物：《Science China(Information Sciences)》 (中国科学:信息科学(英文版))
年 卷 期：2020年第63卷第11期
页 面：248-268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] 080202[工学-机械电子工程] 08[工学] 0804[工学-仪器科学与技术] 0802[工学-机械工程] 0812[工学-计算机科学与技术（可授工学、理学学位）]
基 金：supported by National Key R&D Program of China (Grant No. 2018YFB1201500) National Natural Science Foundation of China (Grant Nos. 61873201, 61773313, U1734210) Key Research and Development Program of Shaanxi Province (Grant No. 2018GY-139) Natural Science Foundation of Shaanxi Provincial Department of Education (Grant No. 19JS051) CERNET Innovation Project (Grant No. NGII20161201) Scientific and Technological Planning Project of Beilin District of Xi’an (Grant No. GX1819)
主 题：bio-robots unmanned system outlier detection hyper-parameters trajectory prediction
摘 要：Accurate monitoring the surrounding environment is an important research direction in the field of unmanned systems such as bio-robotics, and has attracted much research attention in recent years. The trajectories of surrounding vehicles should be predicted accurately in space and time to realize active defense and running safety of an unmanned system. However, there is uncertainty and uncontrollability in the process of trajectory prediction of surrounding obstacles. In this study, we propose a trajectory prediction method based on a sequential model, that fuses two neural networks of a convolutional neural network(CNN) and a long short-term memory network(LSTM). First, a box plot is used to detect and eliminate abnormal values of vehicle trajectories, and valid trajectory data are obtained. Second, the trajectories of surrounding vehicles are predicted by merging the characteristics of CNN space expansion and LSTM time expansion;the hyper-parameters of the model are optimized according to a grid search algorithm, which satisfies the double-precision prediction requirement in space and time. Finally, data from next generation simulation(NGSIM) and Creteil roundabout in France are taken as test cases; the correctness and rationality of the method are verified by prediction error indicators. Experimental results demonstrate that the proposed CNNLSTM method is more accurate and features a shorter time cost, which meets the prediction requirements and provides an effective method for the safe operation of unmanned systems.
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