arXiv:2605. 05092v2 Announce Type: replace-cross Abstract: Safe L2/L3 driving automation requires anticipating human-in-the-loop reactions during shared-control transitions.
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Drive‑HWM introduces a hierarchical slow‑fast world modeling framework for autonomous driving. The slow model predicts multi‑step future representations, while the fast model jointly predicts the next frame and immediate action using a lightweight multimodal backbone and an autoregressive expert. Dynamic‑Aware Latents, learned through optical‑flow prediction, explicitly capture motion dynamics, and experiments on NAVSIM v1 and v2 show strong driving performance with validated ablation studies.
arXiv:2606. 26922v1 Announce Type: cross Abstract: Continuous driver monitoring in automated vehicles requires low-latency inference while avoiding unsafe decisions under uncertain driver states.
arXiv:2603. 22281v2 Announce Type: replace-cross Abstract: Recent progress in latent world models (e.
arXiv:2608. 14767v1 Announce Type: cross Abstract: Automated vehicles must explain their decisions in ways that passengers can understand, monitor, and trust.
arXiv:2606. 28758v1 Announce Type: cross Abstract: Predicting future states is essential for autonomous agents, yet current Vision-Language-Action (VLA) models fundamentally lack this capability, relying instead on reactive perception-action mapping.
arXiv:2606. 17362v1 Announce Type: cross Abstract: Autonomous driving has shifted towards end-to-end policy learning, where reliable, interpretable policy evaluation is a fundamental challenge as driving quality is highly context-dependent.