MORE: Learning Motion-Oriented Registers for Driving World Models and Planning

arXiv:2610.14871 · cs.CV · Submitted 2026-10-10 · Read on arXiv

cs.CV

Submitted: 2026-10-10

Updated: 2026-10-10

Code: https://github.com/Cyril-gyd/MORE

License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/

Terminology

• World Model — 1× in this paper · explained in 7 episodes

Sources

• V-JEPA 2: Self-Supervised Video Models Enable Understanding, Prediction and Planning
• Revisiting Feature Prediction for Learning Visual Representations from Video
• NuPlan: A closed-loop ML-based planning benchmark for autonomous vehicles
• Pseudo-Simulation for Autonomous Driving
• DriveFuture: Future-Aware Latent World Models for Autonomous Driving
• CoWorld-VLA: Thinking in a Multi-Expert World Model for Autonomous Driving
• DriveWorld-VLA: Unified Latent-Space World Modeling with Vision-Language-Action for Autonomous Driving
• NTR: Neural Token Reconstruction for Scene Token Bottleneck in End-to-End Driving
• Hydra-MDP++: Advancing End-to-End Driving via Expert-Guided Hydra-Distillation
• Generalized Trajectory Scoring for End-to-end Multimodal Planning
• The DAWN of World-Action Interactive Models
• V-JEPA 2.1: Unlocking Dense Features in Video Self-Supervised Learning
• What Drives Success in Physical Planning with Joint-Embedding Predictive World Models?
• Latent-WAM: Latent World Action Modeling for End-to-End Autonomous Driving
• Drive-JEPA: Video JEPA Meets Multimodal Trajectory Distillation for End-to-End Driving
• EponaV2: Driving World Model with Comprehensive Future Reasoning
• DreamerAD: Efficient Reinforcement Learning via Latent World Model for Autonomous Driving
• Discrete-WAM: Unified Discrete Vision-Action Token Editing for World-Policy Learning

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