Reliable long-horizon planning remains a key challenge in end-to-end autonomous driving. By accounting for future motion evolution and potential consequences, it provides forward-looking guidance for safe and consistent driving in evolving traffic environments. Existing methods use historical planning states as tempora...
Yuchen Liu, Zi-Ying Song, Shengkai Zhang et al.· 0 citations
MomADv2, a reliable state-space memory framework for long-horizon end-to-end autonomous driving, introduces a Selective State-Space Planning Memory Query Module, which filters historical planning queries based on temporal continuity and command consistency, and models the evolution of planning intentions through a sele...
Zi-Ying Song, Sheng-Kai Zhang, Lin Liu et al.· 0 citations
This work proposes Flow-JEPA (F-JEPA), a conditional flow matching dynamics model that jointly generates a sequence of future latent states conditioned on the current observation and actions, suggesting that conditional flow matching provides a promising alternative to deterministic autoregressive dynamics in JEPA worl...
PhyLatent, a dynamics-relevant training objective for JointEmbedding Predictive Architecture (JEPA) world models, shows that global non-collapse alone is insufficient for learning a reliable JEPA worldmodel state space.
Xi Zeng, Haojie Ren, Ziying Song· 2 citations
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