RankFormer: A Propose-then-Select Transformer for Multi-Agent Multimodal Trajectory Prediction

תקציר מקורי באנגליתarXiv:2604.07126v2 Announce Type: replace-cross Abstract: Predicting vehicle trajectories plays an important role in autonomous driving, transportation safety analysis, traffic operations, etc. Although many deep learning algorithms are devised to predict future vehicle trajectories, the vehicle trajectory prediction problem is still challenging due to the complexity of decision-making process, interactions with surrounding vehicles, and the existence of multiple possible intentions for the traveling agents even under similar scenarios. For modeling interactions between vehicles, previous methods are either limited by specific graph structure (e.g., Graph Neural Network) or limited by fixed labeled intentions. In this study, we propose a pure Transformer-based network considering both temp