关键词: 车辆轨迹预测；GCN；LSTM；注意力机制

Abstract: In response to the issues of insufficient accuracy and poor adaptability in existing vehicle trajectory prediction methods when dealing with complex traffic environments and multi-vehicle interactions, this paper proposes a vehicle trajectory prediction method based on graph convolutional networks (GCN) and attention mechanisms. First, long short-term memory (LSTM) is used to initially encode the historical trajectories of vehicles. Then, GCN is introduced to model spatial relationships and interaction effects between vehicles, updating vehicle features. Subsequently, multi-head self-attention mechanisms further capture global interaction information between vehicles. Finally, residual decoders generate future trajectory predictions. Experimental results show that the model proposed in this paper demonstrates superior vehicle trajectory prediction capabilities on the Argoverse dataset.

Key words: vehicle trajectory prediction; GCN; LSTM; attention mechanism