DA-WAM: Decision-Aligned Future Latents for Driving World Models
cs.RO, cs.AI
Submitted: 2026-08-19
Updated: 2026-10-18
Code: https://github.com/LeapWM/da-wam
License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
Terminology
Sources
- V-JEPA 2: Self-Supervised Video Models Enable Understanding, Prediction and Planning
- DriveFuture: Future-Aware Latent World Models for Autonomous Driving
- Hydra-MDP++: Advancing End-to-End Driving via Expert-Guided Hydra-Distillation
- Zero-Human Demonstration End-to-end Autonomous Driving with Trajectory Scorer
- Generalized Trajectory Scoring for End-to-end Multimodal Planning
- V-JEPA 2.1: Unlocking Dense Features in Video Self-Supervised Learning
- Centaur: Robust End-to-End Autonomous Driving with Test-Time Training
- SparseDriveV2: Scoring is All You Need for End-to-End Autonomous Driving
- Beyond Imitation: Learning Safe End-to-End Autonomous Driving from Hard Negatives
- 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
- Auto-JEPA: A Latent World Model of Continuous Intent for End-to-End Autonomous Driving
- IDOL: Inverse-Dynamics-Guided Future Prediction for End-to-End Autonomous Driving
- DiffusionDriveV2: Reinforcement Learning-Constrained Truncated Diffusion Modeling in End-to-End 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
- ARC: A Reasoning Recipe for Robot Foundation Models
- HRDexDB: A 4D Dexterous Grasping Dataset Across Human and Multiple Robot Embodiments
- APT: Action Expert Pretraining Improves Instruction Generalization of Vision-Language-Action Policies