RankFormer Transformer advances multi-agent trajectory prediction

Researchers have introduced RankFormer, a novel Transformer-based network designed for multi-agent multimodal trajectory prediction. This model addresses challenges in autonomous driving by effectively modeling complex interactions and intentions among vehicles without relying on specific graph structures or labeled intention samples. RankFormer utilizes a cross-modal attention module to learn ordered trajectories and intentions, enhancing spatial encoding with ego-centric velocity and acceleration data from neighboring vehicles. AI

IMPACT Enhances prediction capabilities for autonomous driving systems by modeling complex agent interactions.

RANK_REASON The cluster contains a research paper detailing a new model architecture. [lever_c_demoted from research: ic=1 ai=1.0]

Read on arXiv cs.AI →

• arXiv
• Diyi Liu
• Hugging Face
• RankFormer
• Transformer

• paper
• model release

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