RankFormer: A Propose-then-Select Transformer for Multi-Agent Multimodal Trajectory Prediction
Predicting traffic agent trajectories plays an important role in autonomous driving, traffic operations, transportation safety analysis, etc. Although many deep learning algorithms are devised to predict future agent trajectories, the 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. Most existing methods are limited by the requirement of graph structures (e.g., Graph Neural Network) or the requirement of manually labeled intentions. In this study, we propose a pure Transformer-based deep learnin...
| Authors | Diyi Liu, Zihan Niu, Tu Xu et al. |
|---|---|
| Primary category | cs.RO |
| Published | 8 April 2026 |
| Last updated at source | 11 August 2026 |
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