Vehicle trajectory modeling is an important foundation for urban intelligent services. Trajectory prediction of cars is a hot topic. A model including convolutional neural network(CNN) and long short-term memory(LSTM)...Vehicle trajectory modeling is an important foundation for urban intelligent services. Trajectory prediction of cars is a hot topic. A model including convolutional neural network(CNN) and long short-term memory(LSTM) was proposed, which is named trajectory-CNN-LSTM(TCL). CNN can extract the spatial features of the trajectory in the input image. Besides, LSTM can extract the time-series features of the input trajectory. After that, the model uses fully connected layers to merge the two features for the final predicting. The experiments on the Porto dataset of The European Conference on Machine Learning and Principles and Practice of Knowledge Discovery in Databases(ECML-PKDD) show that the average prediction error of TCL is reduced by 0.15 km, 0.42 km, and 0.39 km compared to the trajectory-convolution(T-CONV), multi-layer perceptron(MLP), and recurrent neural network(RNN) model, respectively.展开更多
文摘海底控制点的GNSS-A定位精度受到测量船相对于海底控制点的航迹影响,本文针对圆形测量模式垂向几何结构较弱的问题,给出了一种新的基于嵌套圆的直线测量模式的分析方法,研究了直线测量模式的参数可估性,并给出了直线测量模式得到唯一解的条件.同时,详细分析了圆形测量模式下要增加十字航迹的原因,推导出圆加十字测量模式下获得海底控制点最优三维点位精度的走航半径约为1.15倍水深.理论分析表明,在圆形测量模式下增加直线航迹能够有效增强其几何结构,提升定位效能.此外,针对是否存在唯一的最优航迹进行了思考,并给出了相应的见解.最后,利用深海实测数据验证了理论推导的结果,圆加十字测量模式较圆形测量模式对海底控制点定位的精度提升可达1.4 cm.
基金supported by the National Key Research and Development Program of China (2017YFB0503700)the Fundamental Research Funds for the Central Universities (2019PTB-010)。
文摘Vehicle trajectory modeling is an important foundation for urban intelligent services. Trajectory prediction of cars is a hot topic. A model including convolutional neural network(CNN) and long short-term memory(LSTM) was proposed, which is named trajectory-CNN-LSTM(TCL). CNN can extract the spatial features of the trajectory in the input image. Besides, LSTM can extract the time-series features of the input trajectory. After that, the model uses fully connected layers to merge the two features for the final predicting. The experiments on the Porto dataset of The European Conference on Machine Learning and Principles and Practice of Knowledge Discovery in Databases(ECML-PKDD) show that the average prediction error of TCL is reduced by 0.15 km, 0.42 km, and 0.39 km compared to the trajectory-convolution(T-CONV), multi-layer perceptron(MLP), and recurrent neural network(RNN) model, respectively.
