GNN-RM: A trajectory completion algorithm based on graph neural networks and regeneration modules

doi:10.1016/j.ijcce.2024.07.001

淡江大學機構典藏 > 工學院 > 資訊工程學系暨研究所 > 期刊論文 > Item 987654321/126218

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题名:	GNN-RM: A trajectory completion algorithm based on graph neural networks and regeneration modules
作者:	Hui, Lin
关键词:	Intelligent connected vehicles;Trajectory completion, Graph neural networks;Multi-head attention mechanism
日期:	2024-07-14
上传时间:	2024-09-20 12:07:22 (UTC+8)
摘要:	Data about vehicle trajectories assumes a crucial role in applications such as intelligent connected vehicles. However, missing values resulting from sensors and other factors frequently affect real trajectory data. Currently, it is challenging to utilize trajectory completion methods to generate accurate real-time results at an affordable computing cost. This paper proposes GNN-RM, a trajectory completion algorithm based on graph neural networks and regeneration modules, encompassing feature extraction, subgraph construction, spatial interaction graph, and trajectory regeneration modules. The feature extraction algorithm extracts influential data as feature vectors based on certain conditions and organizes these feature vectors into different subgraphs according to categories. The spatial interaction graph constructed through graph neural networks extracts spatial interaction features between vehicles and the environment, while the regeneration modules constructed by multi-head attention mechanisms extract temporal features of vehicles, thereby completing the missing trajectories. The experimental results demonstrate that GNN-RM can achieve higher trajectory completion accuracy with fewer input parameters than multiple baseline models.
關聯:	International Journal of Cognitive Computing in Engineering 5,　p.297-306
DOI:	10.1016/j.ijcce.2024.07.001
显示于类别:	[資訊工程學系暨研究所] 期刊論文

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