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cs.CV2026
Latent-WAM++: Shaping World and Action Representations for End-to-End Autonomous Driving
Yichen Zhang, Yupeng Zheng, Linbo Wang +10
We introduce Latent-WAM++, a representation-centered World Action Model (WAM) for end-to-end autonomous driving. Existing WAMs are primarily trained to predict future states or fin…
cs.CV2026
SV-WAM: An Efficient Surround-View World-Action Model for End-to-End Autonomous Driving
Jinyang Wang, Shiwei Li, Junjian Wang +12
World models (WMs) have demonstrated strong potential for end-to-end autonomous driving by learning predictive representations of future scene dynamics. However, generating future…