华中科技大学学报（自然科学版）2024，Vol.52Issue(10)：54-59,6.DOI:10.13245/j.hust.240529
面向船舶智能航行的多目标轨迹预测算法
Multi-target trajectory prediction algorithm for ship intelligent navigation
摘要
Abstract
Regarding the current issues of imbalanced sample distribution,low utilization of group interaction relationships,and prediction results not conforming to vessel kinematics in trajectory prediction algorithms,a multi-objective trajectory prediction algorithm based on Sparse graph convolutional networks(S-GCN)was proposed.First,a learnable non-probabilistic sampling network(NPSN)was designed to generate trajectory samples with balanced distributions.Then,a method for representing vessel clusters was proposed based on the relationships between individual vessels and groups to infer multi-objective interaction modes that comply with maritime rules.Finally,an interactive multiple model(IMM)state estimation algorithm was employed to filter and correct predicted trajectories so as to satisfy vessel kinematic principles.Experimental results show significant improvements in algorithm performance,with average displacement errors(ADE)and final displacement errors(FDE)of 17.06 m and 29.49 m,respectively,outperforming S-GCN and other commonly used prediction algorithms.关键词
智能船舶/多目标轨迹预测/稀疏图卷积网络/非概率采样网络/集群表示/滤波修正Key words
intelligent ship/multi-target trajectory prediction/sparse graph convolution network/non-probability sampling network/group representation/filter correction分类
交通工程引用本文复制引用
徐海祥,卢烨彬,冯辉,周志杰..面向船舶智能航行的多目标轨迹预测算法[J].华中科技大学学报（自然科学版）,2024,52(10):54-59,6.基金项目
国家自然科学基金资助项目(51979210,52371374). （51979210,52371374）
