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cs.CV2025
UniUGP: Unifying Understanding, Generation, and Planing For End-to-end Autonomous Driving
Hao Lu, Ziyang Liu, Guangfeng Jiang +4
Autonomous driving (AD) systems struggle in long-tail scenarios due to limited world knowledge and weak visual dynamic modeling. Existing vision-language-action (VLA)-based methods…
cs.CV2025
WorldLens: Full-Spectrum Evaluations of Driving World Models in Real World
Ao Liang, Lingdong Kong, Tianyi Yan +19
Generative world models are reshaping embodied AI, enabling agents to synthesize realistic 4D driving environments that look convincing but often fail physically or behaviorally. D…
cs.CV2025
4D Driving Scene Generation With Stereo Forcing
Hao Lu, Zhuang Ma, Guangfeng Jiang +6
Current generative models struggle to synthesize dynamic 4D driving scenes that simultaneously support temporal extrapolation and spatial novel view synthesis (NVS) without per-sce…