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cs.CV2026
LAFP: Preserving Latent Action Structure in Latent Policy Learning via Flow Matching
Jiexi Lyu, Xizhou Bu, Qingqiu Huang +4
Learning high-quality latent actions from large-scale unlabeled videos, coupled with limited real-world interaction data for training an action decoder, has emerged as a promising…
cs.CV2026
DriveWorld-VLA: Unified Latent-Space World Modeling with Vision-Language-Action for Autonomous Driving
Feiyang jia, Lin Liu, Ziying Song +4
End-to-end (E2E) autonomous driving has recently attracted increasing interest in unifying Vision-Language-Action (VLA) with World Models to enhance decision-making and forward-loo…
cs.CV2025
GuideFlow: Constraint-Guided Flow Matching for Planning in End-to-End Autonomous Driving
Lin Liu, Caiyan Jia, Guanyi Yu +6
Driving planning is a critical component of end-to-end (E2E) autonomous driving. However, prevailing Imitative E2E Planners often suffer from multimodal trajectory mode collapse, f…