EditWM: Event-Decomposed World Modeling with Incremental Correction for End-to-End Autonomous Driving
cs.RO
Submitted: 2026-09-15
Updated: 2026-09-15
Terminology
Sources
- World Models
- Hydra-MDP: End-to-end Multimodal Planning with Multi-target Hydra-Distillation
- CLOVER: Closed-Loop Value Estimation and Ranking for End-to-End Autonomous Driving Planning
- VADv2: End-to-End Vectorized Autonomous Driving via Probabilistic Planning
- Generalized Trajectory Scoring for End-to-end Multimodal Planning
- DINOv2: Learning Robust Visual Features without Supervision
- Hydra-MDP++: Advancing End-to-End Driving via Expert-Guided Hydra-Distillation
- DiffusionDriveV2: Reinforcement Learning-Constrained Truncated Diffusion Modeling in End-to-End Autonomous Driving
- Pseudo-Simulation for Autonomous Driving
Related papers
- FMT x: An Efficient and Asymptotically Optimal Extension of the Fast Marching Tree for Dynamic Replanning
- MPCFormer: A physics-informed data-driven approach for explainable socially-aware autonomous driving
- RoboLab: A High-Fidelity Simulation Benchmark for Analysis of Task Generalist Policies
- HRDexDB: A 4D Dexterous Grasping Dataset Across Human and Multiple Robot Embodiments
- APT: Action Expert Pretraining Improves Instruction Generalization of Vision-Language-Action Policies
- Fine-tuning is Not Enough: A Parallel Framework for Collaborative Imitation and Reinforcement Learning in End-to-end Autonomous Driving