arXiv AI
WA-JEPA: Rethinking the Video JEPA Paradigm for World-Action Modeling in Autonomous Driving
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arXiv AI
Aug 3
Auto-JEPA: A Latent World Model of Continuous Intent for End-to-End Autonomous Driving
arXiv:2607. 29031v1 Announce Type: cross Abstract: Existing autonomous-driving world models typically perform dense prediction of future videos, occupancy states, BEV representations, or agent motion.
arXiv Computer Vision
Sep 28
WALT: Learning World-Model-Aligned Latent Trajectories for Autonomous Driving
WALT introduces a method to align latent trajectories with pretrained driving world models, creating a compact generative trajectory space that preserves action-relevant semantics without altering the original model. The approach uses a dual-branch autoencoder to map raw waypoints into this latent space and transfers visual world knowledge into trajectory representations. Experiments on NAVSIM benchmarks show modest performance gains and a 30.5% reduction in planner FLOPs, indicating that maintaining world representations while extracting action-relevant information can improve trajectory planning efficiency.
arXiv Machine Learning
3d ago
PlanWAM: Planning-Shaped Future Representations for End-to-End Autonomous Driving
PlanWAM introduces a Planning‑Shaped World Action Model that tailors future‑state representations to be most useful for trajectory planning in end‑to‑end autonomous driving. It compresses historical observations with a Temporal Register Pyramid, shapes a future latent representation using a privileged posterior branch guided by planning objectives, and distills this into a prior branch that predicts the representation from history alone. The resulting foresighted planning context yields state‑of‑the‑art performance on NAVSIM and HUGSIM benchmarks.
arXiv Computer Vision
Aug 25
ReWorld: Representation Learning for World Action Models
arXiv:2606.27504v2 Announce Type: replace Abstract: World Action Models (WAMs) unify future environment prediction with action generation for autonomous driving, yet existing approaches optimize only...
arXiv Computer Vision
Aug 28
SimWAM: A Simple World Action Model for End-to-End Autonomous Driving
SimWAM is a lightweight World-Action Model that uses future‑video prediction only during training to supervise an action expert, enabling end‑to‑end autonomous driving without costly test‑time future imagination. The architecture co‑trains a pretrained video expert and a lightweight action expert via joint flow matching, while an isolated attention mask keeps action prediction independent of future frames. This design allows the video backbone to be swapped and the action expert scaled independently, achieving 91.5 PDMS on NAVSIM, outperforming state‑of‑the‑art WAM planners with lower latency and zero‑shot transfer to nuScenes.
arXiv AI
Jun 30
LWDrive: Layer-Wise World-Model-Guided Vision-Language Model Planning for Autonomous Driving
arXiv:2606. 29879v1 Announce Type: cross Abstract: Vision-Language Models (VLMs) provide powerful semantic understanding and commonsense reasoning for End-to-End Autonomous Driving (E2E-AD) planning.