MomADv2: Reliable Temporal Memory for End-to-End Autonomous Driving
Long-horizon planning is critical for safe autonomous driving in complex scenarios. Existing methods improve planning continuity with temporal memory, but such memory may become invalid and mislead decisions when the driving command changes. Thus, selectively leveraging useful history while suppressing command-inconsistent memory remains a key challenge. To address this issue, we propose MomADv2, a reliable state-space memory framework for long-horizon end-to-end autonomous driving. At its core, MomADv2 introduces a Selective State-Space Planning Memory Query Module, which filters historical p
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- PossiblePossibly related (embedding) · 48%Are Current AI Memory Architectures Optimizing for the Wrong Abstraction? [D] →
- FuzzyOverlapping authors or contributors · 62%ray-project/ray →
“Shared author/contributor keys: wang”
- FuzzyOverlapping authors or contributors · 62%google-research/google-research →
“Shared author/contributor keys: sun”
- FuzzyOverlapping authors or contributors · 62%modular/modular →
“Shared author/contributor keys: liu”
- FuzzyOverlapping authors or contributors · 62%bytedance/deer-flow →
“Shared author/contributor keys: wang”
- FuzzyOverlapping authors or contributors · 62%sgl-project/sglang →
“Shared author/contributor keys: luo”
- LinkedLinked via arxiv author · 85%Ziying Song →
“MomADv2: Reliable Temporal Memory for End-to-End Autonomous Driving”
- LinkedLinked via arxiv author · 85%Shengkai Zhang →
“MomADv2: Reliable Temporal Memory for End-to-End Autonomous Driving”