Author

Shengkai Zhang

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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...

Yuchen Liu, Zi-Ying Song, Shengkai 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