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

2026-08-24

AI Summary

为解决自动驾驶中长期规划的连续性和一致性问题，提出MomADv2框架，通过选择性状态空间记忆和轨迹残差修正方法提升决策可靠性。

Abstract

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 planning queries based on temporal continuity and command consistency, selects planning modes relevant to the current command, and models the evolution of planning intentions through a selective state-space mechanism. To further alleviate local trajectory deviations and error accumulation in long-horizon planning, we design a Flow-Matching Trajectory Residual Refiner. It learns a continuous residual correction field from the refined planning output to the expert trajectory, enabling fine-grained trajectory refinement while preserving the stability of anchor-based planning. Extensive experiments on closed-loop NAVSIM and Bench2Drive, as well as open-loop nuScenes, demonstrate that MomADv2 improves long-horizon planning consistency and reduces the average collision rate by 15.6% over MomAD under 6-second planning.

Problem

• long-horizon planning
• temporal memory
• command consistency
• autonomous driving

Innovation

• Selective State-Space Planning Memory Query Module
• Flow-Matching Trajectory Residual Refiner
• Long-horizon planning
• Command consistency