Author

Shengkai Zhang

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Preprint Sep 2026

MomWorld: Momentum-Aware Latent World Model for Long-Horizon Autonomous Driving

Long-horizon planning enables autonomous vehicles to anticipate scene evolution and potential risks, supporting safe and stable decisions in complex interactions. However, existing methods struggle to propagate motion trends from observed history into the future. Long rollouts based on a single latent state may further...

Zi-Ying Song, Sheng-Kai Zhang, Lei Yang et al. · 0 citations

Preprint Aug 2026

Not All History Helps: Velocity-Aware Selective Memory for Long-Horizon End-to-End Autonomous Driving

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...

Yu-Chen Liu, Zi-Ying Song, Sheng-Kai Zhang et al. · 0 citations

Preprint Aug 2026

MomADv2: Reliable Temporal Memory for End-to-End Autonomous Driving

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