ReDrive: Shaping Representations with World Modeling for End-to-End Driving
cs.CV
Submitted: 2026-09-27
Updated: 2026-09-27
Terminology
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
- RAD-2: Scaling Reinforcement Learning in a Generator-Discriminator Framework
- DriveFuture: Future-Aware Latent World Models for Autonomous Driving
- CoWorld-VLA: Thinking in a Multi-Expert World Model for Autonomous Driving
- Senna: Bridging Large Vision-Language Models and End-to-End Autonomous Driving
- AlphaDrive: Unleashing the Power of VLMs in Autonomous Driving via Reinforcement Learning and Reasoning
- Metis: A Generalizable and Efficient World-Action Model for Autonomous Driving and Urban Navigation
- SGDrive: Scene-to-Goal Hierarchical World Cognition for Autonomous Driving
- Hydra-MDP++: Advancing End-to-End Driving via Expert-Guided Hydra-Distillation
- Hydra-MDP: End-to-end Multimodal Planning with Multi-target Hydra-Distillation
- Generalized Trajectory Scoring for End-to-end Multimodal Planning
- The DAWN of World-Action Interactive Models
- DINOv2: Learning Robust Visual Features without Supervision
- DINOv3
- Senna-2: Aligning VLM and End-to-End Driving Policy for Consistent Decision Making and Planning
- Drive-JEPA: Video JEPA Meets Multimodal Trajectory Distillation for End-to-End Driving
- Latent-WAM: Latent World Action Modeling for End-to-End Autonomous Driving
- ReWorld: Learning Better Representations for World Action Models
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