Rectify, Don't Regret: On-Policy Closed-Loop Training for Multimodal Trajectory Prediction
cs.RO
Submitted: 2026-03-24
Updated: 2026-09-18
Terminology
Sources
- Scene Transformer: A unified architecture for predicting multiple agent trajectories
- Argoverse 2: Next Generation Datasets for Self-Driving Perception and Forecasting
- UniMM: A Unified Mixture Model Framework for Multi-Agent Simulation
- Wayformer: Motion Forecasting via Simple & Efficient Attention Networks
- SEPT: Towards Efficient Scene Representation Learning for Motion Prediction
- Trajeglish: Traffic Modeling as Next-Token Prediction
- DONUT: A Decoder-Only Model for Trajectory Prediction
- Closing the Loop: Motion Prediction Models beyond Open-Loop Benchmarks
- CASPNet++: Joint Multi-Agent Motion Prediction
- SemanticFormer: Holistic and Semantic Traffic Scene Representation for Trajectory Prediction using Knowledge Graphs
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