RankFormer: Transformer-Based Multi-Agent Trajectory Prediction
A new deep learning model, RankFormer, has been proposed for multi-modal trajectory prediction in autonomous driving and traffic safety. The model, detailed in arXiv paper 2604.07126, employs a pure Transformer architecture to handle temporal dependencies and agent-agent spatial interactions without relying on graph structures or manually labeled intentions. It uses two parallel decoders to generate trajectories and probabilities, addressing challenges such as complex decision-making and multiple possible intentions. The approach aims to improve prediction accuracy in dynamic traffic scenarios.
Key facts
- RankFormer is a pure Transformer-based model for multi-modal trajectory prediction.
- It addresses challenges in autonomous driving, traffic operations, and transportation safety analysis.
- The model encodes historical trajectories and uses two parallel decoders for trajectory and probability generation.
- It does not require graph structures or manually labeled intentions.
- The paper is available on arXiv with identifier 2604.07126.
- The model considers temporal dependencies and agent-agent spatial interactions.
Entities
Institutions
- arXiv